Access to and use of Data for better Healthcare: A Plea for a cooperative data and Research Infrastructure of Statutory and Private Health Insurers and the Network University Medicine (NUM) Datennutzung für eine bessere Gesundheitsversorgung-Plädoyer für eine kooperative Forschungsdatenplattform der gesetzlichen und privaten Krankenversicherung und dem Netzwerk Universitätsmedizin (NUM) - Schmitt, Jochen; Ihle, Peter; Schoffer, Olaf; Reese, Jens Peter; Ortmann, Steffen; Swart, Enno; Hanß, Sabine; Hoffmann, Falk; Stallmann, Christoph; Kraus, Monika; Semler, Sebastian Claudius; Heyder, Ralf; et al. (Gesundheitswesen, 2024)
With the Network of University Medicine (NUM) and the Medical Informatics Initiative (MII), the BMBF is funding two pioneering, structure-building research measures that are now being merged. The data integration centers (DIZ) of the MII are to be consolidated in the NUM. The aim is to establish a standardized research infrastructure within which the existing data from the clinical routine care of the 36 German university hospitals, from clinical cohorts and clinical-epidemiological studies can be used for various research questions upon request and via coordinated processes. The legal basis for this was the MII's "Informed Broad Consent" , which had been agreed upon with ethics committees and data protection authorities and implemented in all NUM locations, with a so-called "health insurance module" that allows the collection and linking of routine medical data from statutory health insurance funds (GKV) and private health insurers (PKV) as a category of care-related data (VeDa). Linking this routine data with data from hospital information systems offers particularly high potential, as no single data source provides a complete picture of medical care and the two data sources complement each other optimally. The aim now is to integrate this routine data into the NUM's secure, transparent and participatory research infrastructure in a strategic partnership with statutory health insurance funds and private health insurance companies. This promotes Germany in its role as a research location and makes a decisive contribution to improving the quality and safety of healthcare in Germany in an evidence-based manner.
CRIS-Team Scopus Importer:2025-02-04
Keywords: consent; health services research; infra structure; linkage; routine healthcare data; university medicine

Algorithmic Summaries of Perioperative Blood Pressure Fluctuations - Toddenroth, Dennis; Ganslandt, Thomas; Drescher, Caroline; Weith, Thomas; Prokosch, Hans-Ulrich; Schüttler, Jürgen; Münster, Tino (Studies in health technology and informatics, 2016)
Automated perioperative measurements such as cardiovascular monitoring data are commonly compared to established upper and lower thresholds, but could also allow for more complex interpretations. Analyzing such time series in extensive electronic medical records for research purposes may itself require customized automation, so we developed a set of algorithms for quantifying different aspects of temporal fluctuations. We implemented conventional measures of dispersion, summaries of absolute gradients between successive values, and Poincaré plots. We aggregated the severity and duration of hypotensive episodes by calculating the average area under different mean arterial pressure (MAP) thresholds. We applied these methods to 30,452 de-identified MAP series, and analyzed the similarity between alternative indices via hierarchical clustering. To explore the potential utility of these propositional metrics, we computed their statistical association with presumed complications due to cardiovascular instability. We observed that hierarchical clustering reliably segregated features that had been designed to quantify dissimilar aspects. Summaries of temporary hypotension turned out to be significantly increased among patient subgroups with subsequent signs of a complicated recovery. These associations were even stronger for measures that were specifically geared to capturing short-term MAP variability. These observations suggest the potential capability of our proposed algorithms for quantifying heterogeneous aspects of short-term MAP fluctuations. Future research might also target a wider selection of outcomes and other attributes that may be subject to intraoperative variability.
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Keywords: Advanced algorithmic analyses; Clinical times series; EMR reuse

Ansatz und Risikoanalyse für ein Smart Object Network im Krankenhaus - Sedlmayr, Martin; Becker, Andreas; Münch, Ulli; Meier, Fritz; Prokosch, Hans-Ulrich; Ganslandt, Thomas (INFORMATIK 2009 - Im Focus das Leben, Beitrage der 39. Jahrestagung der Gesellschaft fur Informatik e.V. (GI), 2009)
CRIS-Team Scopus Importer:2021-11-16

Ansatz und Risikoanalyse für ein Smart Object Network im Krankenhaus - Sedlmayr, Martin; Becker, Andreas; Münch, Ulli; Meier, Fritz; Prokosch, Hans-Ulrich; Ganslandt, Thomas (Mobiles Computing in der Medizin - Proceedings zum 9. Workshop der GI- und GMDS-Arbeitsgruppe Mobile Informationstechnologie in der Medizin, MoCoMed 2009, 2009)
CRIS-Team Scopus Importer:2021-11-16

Applied Practice and Possible Leverage Points for Information Technology Support for Patient Screening in Clinical Trials: Qualitative Study - Becker, Linda; Ganslandt, Thomas; Prokosch, Hans-Ulrich; Newe, Axel (JMIR Medical Informatics, 2020)
Background: Clinical trials are one of the most challenging and meaningful designs in medical research. One essential step before starting a clinical trial is screening, that is, to identify patients who fulfill the inclusion criteria and do not fulfill the exclusion criteria. The screening step for clinical trials might be supported by modern information technology (IT). Objective: This explorative study aimed (1) to obtain insights into which tools for feasibility estimations and patient screening are actually used in clinical routine and (2) to determine which method and type of IT support could benefit clinical staff. Methods: Semistandardized interviews were conducted in 5 wards (cardiology, gynecology, gastroenterology, nephrology, and palliative care) in a German university hospital. Of the 5 interviewees, 4 were directly involved in patient screening. Three of them were clinicians, 1 was a study nurse, and 1 was a research assistant. Results: The existing state of study feasibility estimation and the screening procedure were dominated by human communication and estimations from memory, although there were many possibilities for IT support. Success mostly depended on the experience and personal motivation of the clinical staff. Electronic support has been used but with little importance so far. Searches in ward-specific patient registers (databases) and searches in clinical information systems were reported. Furthermore, free-text searches in medical reports were mentioned. For potential future applications, a preference for either proactive or passive systems was not expressed. Most of the interviewees saw the potential for the improvement of the actual systems, but they were also largely satisfied with the outcomes of the current approach. Most of the interviewees were interested in learning more about the various ways in which IT could support and relieve them in their clinical routine. Conclusions: Overall, IT support currently plays a minor role in the screening step for clinical trials. The lack of IT usage and the estimations made from memory reported by all the participants might constrain cognitive resources, which might distract from clinical routine. We conclude that electronic support for the screening step for clinical trials is still a challenge and that education of the staff about the possibilities for electronic support in clinical trials is necessary.<br
Keywords: clinical trial; patient screening; electronic support; clinical information systems; inclusion criteria; exclusion criteria; feasibility studies; mobile phone

Applying the RDA Data Maturity Model on the Core Dataset of the German Medical Informatics Initiative - Michaelis, Lea; Inau, Esther Thea; Muzoora, Michael Rusongoza; Wodke, Judith A.H.; Ganslandt, Thomas; Thun, Sylvia; Waltemath, Dagmar (Proceedings of the 15th International Conference on Semantic Web Applications and Tools for Health Care and Life Sciences (SWAT4HCLS 2024), 2024)
The German Medical Informatics Initiative authored a modularised Core Dataset which enables the sharing of clinical routine data across German hospital sites. In this work, we apply the RDA Data Maturity Model to run a FAIR assessment on one module of the Core Dataset. We summarise the results of this assessment and present the lessons learnt. We anticipate that the assessment will help to design a FAIRification workflow for the Core Dataset and to estimate the overall resources required for that task.
CRIS-Team Scopus Importer:2025-02-04
Keywords: FAIR assessment; FAIR Data Maturity Model; FAIR-IMPACT; Medical Informatics Initiative

Approaches and Criteria for Provenance in Biomedical Data Sets and Workflows: Protocol for a Scoping Review. - Gierend, Kerstin; Krueger, Frank; Waltemath, Dagmar; Fuenfgeld, Maximilian; Ganslandt, Thomas; Zeleke, Atinkut Alamirrew (JMIR Research Protocols, 2021)
BACKGROUND: Provenance supports the understanding of data genesis, and it is a key factor to ensure the trustworthiness of digital objects containing (sensitive) scientific data. Provenance information contributes to a better understanding of scientific results and fosters collaboration on existing data as well as data sharing. This encompasses defining comprehensive concepts and standards for transparency and traceability, reproducibility, validity, and quality assurance during clinical and scientific data workflows and research. OBJECTIVE: The aim of this scoping review is to investigate existing evidence regarding approaches and criteria for provenance tracking as well as disclosing current knowledge gaps in the biomedical domain. This review covers modeling aspects as well as metadata frameworks for meaningful and usable provenance information during creation, collection, and processing of (sensitive) scientific biomedical data. This review also covers the examination of quality aspects of provenance criteria. METHODS: This scoping review will follow the methodological framework by Arksey and O'Malley. Relevant publications will be obtained by querying PubMed and Web of Science. All papers in English language will be included, published between January 1, 2006 and March 23, 2021. Data retrieval will be accompanied by manual search for grey literature. Potential publications will then be exported into a reference management software, and duplicates will be removed. Afterwards, the obtained set of papers will be transferred into a systematic review management tool. All publications will be screened, extracted, and analyzed: title and abstract screening will be carried out by 4 independent reviewers. Majority vote is required for consent to eligibility of papers based on the defined inclusion and exclusion criteria. Full-text reading will be performed independently by 2 reviewers and in the last step, key information will be extracted on a pretested template. If agreement cannot be reached, the conflict will be resolved by a domain expert. Charted data will be analyzed by categorizing and summarizing the individual data items based on the research questions. Tabular or graphical overviews will be given, if applicable. RESULTS: The reporting follows the extension of the Preferred Reporting Items for Systematic reviews and Meta-Analyses statements for Scoping Reviews. Electronic database searches in PubMed and Web of Science resulted in 469 matches after deduplication. As of September 2021, the scoping review is in the full-text screening stage. The data extraction using the pretested charting template will follow the full-text screening stage. We expect the scoping review report to be completed by February 2022. CONCLUSIONS: Information about the origin of healthcare data has a major impact on the quality and the reusability of scientific results as well as follow-up activities. This protocol outlines plans for a scoping review that will provide information about current approaches, challenges, or knowledge gaps with provenance tracking in biomedical sciences. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/31750.
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Arbeitsgruppe Interoperabilität: Kerndatensatz und Informationssysteme für Integration und Austausch von Daten in der Medizininformatik-Initiative - Ammon, Danny; Kurscheidt, Maximilian; Buckow, Karoline; Kirsten, Toralf; Löbe, Matthias; Meineke, Frank; Prasser, Fabian; Saß, Julian; Sax, Ulrich; Stäubert, Sebastian; Thun, Sylvia; Wettstein, Reto; et al. (Bundesgesundheitsblatt, Gesundheitsforschung, Gesundheitsschutz, 2024)
The interoperability Working Group of the Medical Informatics Initiative (MII) is the platform for the coordination of overarching procedures, data structures, and interfaces between the data integration centers (DIC) of the university hospitals and national and international interoperability committees. The goal is the joint content-related and technical design of a distributed infrastructure for the secondary use of healthcare data that can be used via the Research Data Portal for Health. Important general conditions are data privacy and IT security for the use of health data in biomedical research. To this end, suitable methods are used in dedicated task forces to enable procedural, syntactic, and semantic interoperability for data use projects. The MII core dataset was developed as several modules with corresponding information models and implemented using the HL7® FHIR® standard to enable content-related and technical specifications for the interoperable provision of healthcare data through the DIC. International terminologies and consented metadata are used to describe these data in more detail. The overall architecture, including overarching interfaces, implements the methodological and legal requirements for a distributed data use infrastructure, for example, by providing pseudonymized data or by federated analyses. With these results of the Interoperability Working Group, the MII is presenting a future-oriented solution for the exchange and use of healthcare data, the applicability of which goes beyond the purpose of research and can play an essential role in the digital transformation of the healthcare system.
CRIS-Team Scopus Importer:2024-06-21
Keywords: Data integration center; FHIR; Research data infrastructure; Secondary Use; Terminology services

Case-based visualization of a patient cohort using SEER epidemiologic data - Maier, Christian; Bürkle, Thomas; Prokosch, Hans-Ulrich; Ganslandt, Thomas (Studies in health technology and informatics, 2014)
Data from cancer registries can be used to track the epidemiology of cancer and can potentially serve to guide individual diagnostic and treatment decisions. Even though some cancer registry datasets have been made publicly available for scientific and clinical use, few applications have so far provided direct access to these data from within the patient context of an electronic patient record. The goal of this project was to implement a proof-of-concept integration of the public SEER (Surveillance, Epidemiology and End Results) cancer registry dataset with a digital breast cancer tumor board at a German university hospital and to determine its utility in the clinical settings. The integration was successfully established, using data from routine documentation to provide dynamic visualizations of cohort composition and Kaplan-Meier survival plots. Evaluation feedback was favorable regarding the concept and implementation, but highlighted that important data elements, e.g. receptor status data, were missing in the SEER dataset, limiting clinical value of the system.
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Clinical Information Systems as the Backbone of a Complex Information Logistics Process: Findings from the Clinical Information Systems Perspective for 2016 - Hackl, W. O.; Ganslandt, Thomas (IMIA Yearbook of Medical Informatics, 2017)
Objective: To summarize recent research and to propose a selection of best papers published in 2016 in the field of Clinical Information Systems (CIS). Method: The query used to retrieve the articles for the CIS section of the 2016 edition of the IMIA Yearbook of Medical Informatics was reused. It again aimed at identifying relevant publications in the field of CIS from PubMed and Web of Science and comprised search terms from the Medical Subject Headings (MeSH) catalog as well as additional free text search terms. The retrieved articles were categorized in a multi-pass review carried out by the two section editors. The final selection of candidate papers was then peer-reviewed by Yearbook editors and external reviewers. Based on the review results, the best papers were then chosen at the selection meeting with the IMIA Yearbook editorial board. Text mining, term co-occurrence mapping, and topic modelling techniques were used to get an overview on the content of the retrieved articles. Results: The query was carried out in mid-January 2017, yielding a consolidated result set of 2,190 articles published in 921 different journals. Out of them, 14 papers were nominated as candidate best papers and three of them were finally selected as the best papers of the CIS field. The content analysis of the articles revealed the broad spectrum of topics covered by CIS research. Conclusions: The CIS field is multi-dimensional and complex. It is hard to draw a well-defined outline between CIS and other domains or other sections of the IMIA Yearbook. The trends observed in the previous years are progressing. Clinical information systems are more than just sociotechnical systems for data collection, processing, exchange, presentation, and archiving. They are the backbone of a complex, trans-institutional information logistics process.
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Common data elements for secondary use of electronic health record data for clinical trial execution and serious adverse event reporting - Bruland, Philipp; Mcgilchrist, Mark; Zapletal, Eric; Acosta, Dionisio; Proeve, Johann; Askin, Scott; Ganslandt, Thomas; Doods, Justin; Dugas, Martin (Bmc Medical Research Methodology, 2016)
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Keywords: Clinical trials; Common data elements; Data quality; Electronic health records; Metadata; Secondary use

Comparative analysis of machine learning algorithms for computer-assisted reporting based on fully automated cross-lingual RadLex mappings - Maros, Mate E.; Cho, Chang Gyu; Junge, Andreas G.; Kaempgen, Benedikt; Saase, Victor; Siegel, Fabian; Trinkmann, Frederik; Ganslandt, Thomas; Groden, Christoph; Wenz, Holger (Scientific Reports, 2021)
Computer-assisted reporting (CAR) tools were suggested to improve radiology report quality by context-sensitively recommending key imaging biomarkers. However, studies evaluating machine learning (ML) algorithms on cross-lingual ontological (RadLex) mappings for developing embedded CAR algorithms are lacking. Therefore, we compared ML algorithms developed on human expert-annotated features against those developed on fully automated cross-lingual (German to English) RadLex mappings using 206 CT reports of suspected stroke. Target label was whether the Alberta Stroke Programme Early CT Score (ASPECTS) should have been provided (yes/no:154/52). We focused on probabilistic outputs of ML-algorithms including tree-based methods, elastic net, support vector machines (SVMs) and fastText (linear classifier), which were evaluated in the same 5 × fivefold nested cross-validation framework. This allowed for model stacking and classifier rankings. Performance was evaluated using calibration metrics (AUC, brier score, log loss) and -plots. Contextual ML-based assistance recommending ASPECTS was feasible. SVMs showed the highest accuracies both on human-extracted- (87%) and RadLex features (findings:82.5%; impressions:85.4%). FastText achieved the highest accuracy (89.3%) and AUC (92%) on impressions. Boosted trees fitted on findings had the best calibration profile. Our approach provides guidance for choosing ML classifiers for CAR tools in fully automated and language-agnostic fashion using bag-of-RadLex terms on limited expert-labelled training data.
CRIS-Team Scopus Importer:2021-11-16

Computer-aided diagnosis in multiparametric mri of the prostate: An open-access online tool for lesion classification with high accuracy - Ellmann, Stephan; Schlicht, Michael; Dietzel, Matthias; Janka, Rolf Matthias; Hammon, Matthias; Saake, Marc; Ganslandt, Thomas; Hartmann, Arndt; Kunath, Frank; Wullich, Bernd; Uder, Michael; Bäuerle, Tobias (Cancers, 2020)
Computer-aided diagnosis (CADx) approaches could help to objectify reporting on prostate mpMRI, but their use in many cases is hampered due to common-built algorithms that are not publicly available. The aim of this study was to develop an open-access CADx algorithm with high accuracy for classification of suspicious lesions in mpMRI of the prostate. This retrospective study was approved by the local ethics commission, with waiver of informed consent. A total of 124 patients with 195 reported lesions were included. All patients received mpMRI of the prostate between 2014 and 2017, and transrectal ultrasound (TRUS)-guided and targeted biopsy within a time period of 30 days. Histopathology of the biopsy cores served as a standard of reference. Acquired imaging parameters included the size of the lesion, signal intensity (T2w images), diffusion restriction, prostate volume, and several dynamic parameters along with the clinical parameters patient age and serum PSA level. Inter-reader agreement of the imaging parameters was assessed by calculating intraclass correlation coefficients. The dataset was stratified into a train set and test set (156 and 39 lesions in 100 and 24 patients, respectively). Using the above parameters, a CADx based on an Extreme Gradient Boosting algorithm was developed on the train set, and tested on the test set. Performance optimization was focused on maximizing the area under the Receiver Operating Characteristic curve (ROCAUC). The algorithm was made publicly available on the internet. The CADx reached an ROCAUC of 0.908 during training, and 0.913 during testing (p = 0.93). Additionally, established rule-in and rule-out criteria allowed classifying 35.8% of the malignant and 49.4% of the benign lesions with error rates of <2%. All imaging parameters featured excellent inter-reader agreement. This study presents an open-access CADx for classification of suspicious lesions in mpMRI of the prostate with high accuracy. Applying the provided rule-in and rule-out criteria might facilitate to further stratify the management of patients at risk.
CRIS-Team Scopus Importer:2020-09-18
Keywords: Artificial intelligence; Computer-aided diagnosis; Machine learning; Prostate cancer; Prostate mpMRI

Computerized patient identification for the EMBRACA clinical trial using real-time data from the PRAEGNANT network for metastatic breast cancer patients - Hein, Alexander; Gaß, Paul; Walter, Christina Barbara; Taran, Florin-Andrei; Hartkopf, Andreas; Overkamp, Friedrich; Kolberg, Hans-Christian; Hadji, Peyman; Tesch, Hans; Ettl, Johannes; Wuerstlein, Rachel; Lounsbury, Debra; et al. (Breast Cancer Research and Treatment, 2016)
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Keywords: BRCA1; BRCA2; Breast cancer; Clinical trial eligibility; PARP inhibitors; Talazoparib

Concept and implementation of a study dashboard module for a continuous monitoring of trial recruitment and documentation - Toddenroth, Dennis; Sivagnanasundaram, Janakan; Prokosch, Hans-Ulrich; Ganslandt, Thomas (Journal of Biomedical Informatics, 2016)
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Keywords: Data management for clinical trials; Procedures for maintaining databases; Workflows for data and safety monitoring

Correlation of mammographic density and serum calcium levels in patients with primary breast cancer - Hack, Carolin; Stoll, Martin; Jud, Sebastian; Heusinger, Katharina; Adler, Werner; Haeberle, Lothar; Ganslandt, Thomas; Heindl, Felix; Schulz-Wendtland, Rüdiger; Uder, Michael; Beckmann, Matthias; Fasching, Peter; et al. (Cancer Medicine, 2017)
Percentage mammographic breast density (PMD) is one of the most important risk factors for breast cancer (BC). Calcium, vitamin D, bisphosphonates, and denosumab have been considered and partly confirmed as factors potentially influencing the risk of BC. This retrospective observational study investigated the association between serum calcium level and PMD. A total of 982 BC patients identified in the research database at the University Breast Center for Franconia with unilateral BC, calcium and albumin values, and mammogram at the time of first diagnosis were included. PMD was assessed, using a semiautomated method by two readers. Linear regression analyses were conducted to investigate the impact on PMD of the parameters of serum calcium level adjusted for albumin level, and well-known clinical predictors such as age, body mass index (BMI), menopausal status and confounder for serum calcium like season in which the BC was diagnosed. Increased calcium levels were associated with reduced PMD (P = 0.024). Furthermore, PMD was inversely associated with BMI (P < 0.001) and age (P < 0.001). There was also an association between PMD and menopausal status (P < 0.001). The goodness-of-fit of the regression model was moderate. This is the first study assessing the association between serum calcium level and PMD. An inverse association with adjusted serum calcium levels was observed. These findings add to previously published data relating to vitamin D, bisphosphonates, denosumab, and the RANK/RANKL signaling pathway in breast cancer risk and prevention.
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Das TMF-Datenschutzkonzept für medizinische Daten-sammlungen und Biobanken - Pommerening, Klaus; Drepper, Johannes; Ganslandt, Thomas; Helbing, Krister; Müller, Thomas; Sax, Ulrich; Semler, Sebastian; Speer, Ronald (INFORMATIK 2009 - Im Focus das Leben, Beitrage der 39. Jahrestagung der Gesellschaft fur Informatik e.V. (GI), 2009)
CRIS-Team Scopus Importer:2021-11-16

Data mining of reference intervals for coagulation screening tests in adult patients - Zierk, Jakob; Ganslandt, Thomas; Rauh, Manfred; Metzler, Markus; Strasser, Erwin (Clinica Chimica Acta, 2019)
Background: Appropriate reference intervals are essential when evaluating laboratory test results. However, establishment of reference intervals is challenging, especially for coagulation screening tests, and uncertainty exists regarding age- and sex-dependency of test results. Data mining of laboratory information systems is an emerging approach to reference interval determination, and we evaluated its applicability to coagulation tests. Methods: We analyzed measurements of activated partial thromboplastin time (aPTT), prothrombin time (PT), international normalized ratio (INR), thrombin time (TT), and fibrinogen performed during clinical care in the University Hospital Erlangen, Germany (1,778,738 samples from 116,754 adult patients, 45,577-509,859 samples per analyte). We identified the proportion of samples from healthy individuals using an established statistical approach (Reference Limit Estimator), in which the distribution of physiological test results is approximated using a parametrical function, and used for the calculation of reference intervals. Results: We established age- and sex specific reference intervals for aPTT, PT, INR, TT, and fibrinogen, and created batch- and reagent-specific aPTT-reference intervals. Additionally, we evaluated the sensitivity of the established aPTT reference intervals for the detection of factor VIII, IX, XI, XII deficiencies. Conclusion: Data mining of laboratory test results allows the creation of age- and sex-reference intervals for coagulation tests that are specific to the examined population, analytical framework, and reagent. This approach can complement conventional methods when establishing reference intervals and improve clinical decision-making based on coagulation tests. The reference intervals established in this study show only minor variation with sex and age, supporting the practice of providing a common reference interval for adult women and men.
CRIS-Team Scopus Importer:2019-09-24
Keywords: Coagulation screening tests; Data mining; Reference intervals

Data Quality– and Utility-Compliant Anonymization of Common Data Model–Harmonized Electronic Health Record Data: Protocol for a Scoping Review - Wabo, Gaetan Kamdje; Prasser, Fabian; Gierend, Kerstin; Siegel, Fabian; Ganslandt, Thomas (JMIR Research Protocols, 2023)
Background: The anonymization of Common Data Model (CDM)–converted EHR data is essential to ensure the data privacy in the use of harmonized health care data. However, applying data anonymization techniques can significantly affect many properties of the resulting data sets and thus biases research results. Few studies have reviewed these applications with a reflection of approaches to manage data utility and quality concerns in the context of CDM-formatted health care data. Objective: Our intended scoping review aims to identify and describe (1) how formal anonymization methods are carried out with CDM-converted health care data, (2) how data quality and utility concerns are considered, and (3) how the various CDMs differ in terms of their suitability for recording anonymized data. Methods: The planned scoping review is based on the framework of Arksey and O'Malley. By using this, only articles published in English will be included. The retrieval of literature items should be based on a literature search string combining keywords related to data anonymization, CDM standards, and data quality assessment. The proposed literature search query should be validated by a librarian, accompanied by manual searches to include further informal sources. Eligible articles will first undergo a deduplication step, followed by the screening of titles. Second, a full-text reading will allow the 2 reviewers involved to reach the final decision about article selection, while a domain expert will support the resolution of citation selection conflicts. Additionally, key information will be extracted, categorized, summarized, and analyzed by using a proposed template into an iterative process. Tabular and graphical analyses should be addressed in alignment with the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) checklist. We also performed some tentative searches on Web of Science for estimating the feasibility of reaching eligible articles. Results: Tentative searches on Web of Science resulted in 507 nonduplicated matches, suggesting the availability of (potential) relevant articles. Further analysis and selection steps will allow us to derive a final literature set. Furthermore, the completion of this scoping review study is expected by the end of the fourth quarter of 2023. Conclusions: Outlining the approaches of applying formal anonymization methods on CDM-formatted health care data while taking into account data quality and utility concerns should provide useful insights to understand the existing approaches and future research direction based on identified gaps. This protocol describes a schedule to perform a scoping review, which should support the conduction of follow-up investigations.
CRIS-Team Scopus Importer:2023-09-15
Keywords: common data model; data anonymization; data privacy models; data quality; data standard; EHR; electronic health record

Deep learning frameworks for rapid gram stain image data interpretation: Protocol for a retrospective data analysis - Kim, Hee; Ganslandt, Thomas; Miethke, Thomas; Neumaier, Michael; Kittel, Maximilian (JMIR Research Protocols, 2020)
Background: In recent years, remarkable progress has been made in deep learning technology and successful use cases have been introduced in the medical domain. However, not many studies have considered high-performance computing to fully appreciate the capability of deep learning technology. Objective: This paper aims to design a solution to accelerate an automated Gram stain image interpretation by means of a deep learning framework without additional hardware resources. Methods: We will apply and evaluate 3 methodologies, namely fine-tuning, an integer arithmetic-only framework, and hyperparameter tuning. Results: The choice of pretrained models and the ideal setting for layer tuning and hyperparameter tuning will be determined. These results will provide an empirical yet reproducible guideline for those who consider a rapid deep learning solution for Gram stain image interpretation. The results are planned to be announced in the first quarter of 2021. Conclusions: Making a balanced decision between modeling performance and computational performance is the key for a successful deep learning solution. Otherwise, highly accurate but slow deep learning solutions can add value to routine care.
CRIS-Team Scopus Importer:2021-11-16
Keywords: Convolutional neural network; Deep learning; High performance computing; Image data analysis; Rapid Gram stain classification

Digital communication strategies in gastroenterology: Position paper of the LebensBlicke Foundation Digitale Kommunikationsstrategien in der Gastroenterologie - Riemann, Jürgen Ferdinand; Teufel, Andreas; Ganslandt, Thomas; Hann, Alexander; Hildebrandt, Helmut; Jütte, Hendrik; Meining, Alexander; Meyer, Hansjörg; Naumann, Axel; Opitz, Oliver; Schilling, Dieter; Hüppe, Dietrich (Zeitschrift für Gastroenterologie, 2021)
CRIS-Team Scopus Importer:2021-11-16

Digitale Versorgungsformen zur Personalisierung der stationsäquivalenten Behandlung - Rauschenberg, Christian; Hirjak, Dusan; Ganslandt, Thomas; Schulte-Strathaus, Julia C. C.; Schick, Anita; Meyer-Lindenberg, Andreas; Reininghaus, Ulrich (Der Nervenarzt, 2021)
Background: Ward-equivalent treatment (StäB), a form of crisis resolution and home treatment in Germany, has been introduced in 2018 as a new model of mental health service delivery for people with an indication for inpatient care. The rapid progress in the field of information and communication technology offers entirely new opportunities for innovative digital mental health care, such as telemedicine, eHealth, or mHealth interventions. Objective: This review aims to provide a comprehensive overview of novel digital forms of service delivery that may contribute to a personalized delivery of StäB and improving clinical and social outcomes as well as reducing direct and indirect costs. Method: This work is based on a narrative review. Results: Four primary digital forms of service delivery have been identified that can be used for personalized delivery of StäB: (1) communication, continuity of care, and flexibility through online chat and video call; (2) monitoring of symptoms and behavior in real-time through ecological momentary assessment (EMA); (3) use of multimodal EMA data to generate and offer personalized feedback on subjective experience and behavioral patterns as well as (4) adaptive ecological momentary interventions (EMI) tailored to the person, moment, and context in daily life. Conclusion: New digital forms of service delivery have considerable potential to increase the effectiveness and cost-effectiveness of crisis resolution, home treatment, and assertive outreach. An important next step is to model and initially evaluate these novel digital forms of service delivery in the context of StäB and carefully investigate their quality from the user perspective, safety, feasibility, initial process and outcome quality as well as barriers and facilitators of implementation.
CRIS-Team Scopus Importer:2021-11-16
Keywords: Digital intervention; Ecological momentary intervention; eHealth; Mobile health; Telemedicine

Digital Follow-Up and the Perspective of Patient-Centered Care in Oncology: What's the PROblem? - Giordano, Frank A.; Welzel, Grit; Siefert, Victor; Jahnke, Lennart; Ganslandt, Thomas; Wenz, Frederik; Grosu, Anca-Ligia; Heinemann, Felix; Nicolay, Nils H. (Oncology, 2020)
There is accumulating evidence from randomized trials suggesting that digital patient-centered care allows a more reliable detection of tumour-related symptoms and adverse events -with a direct impact on overall survival. Consequently, a variety of unsynchronized approaches were kicked off to (electronically) measure patient-reported outcomes (PROs). Despite increasing evidence that PRO data are highly relevant for patient care, the data generated in these initial projects lack standardized processing pathways in order to impact clinical routine; therefore, potential future routine PRO assessments require adequate analysis, storage and processing to allow a robust, reproducible and reliable incorporation into routine clinical decision-making. Here, we discuss relevant challenges of digital follow-up that need to be tackled to render PRO data as relevant to physicians as laboratory or biomarker data.
CRIS-Team Scopus Importer:2021-11-16
Keywords: Digital care; Digital follow-up; Patient-reported outcomes

Digital networks for laboratory data: Potentials, barriers and current initiatives - Ganslandt, Thomas; Neumaier, Michael (Clinical Chemistry and Laboratory Medicine, 2019)
Medical care is increasingly delivered by multiple providers across healthcare sectors and specialties, leading to a fragmentation of the electronic patient record across organizations and vendor IT systems. The rapid uptake of wearables and connected diagnostic devices adds another source of densely collected data by the patients themselves. Integration of these data sources opens up several potentials: a longitudinal view of laboratory findings would close the gaps between individual provider visits and allow to more closely follow disease progression. Adding non-laboratory data (e.g. diagnoses, procedures) would add context and support clinical interpretation of findings. Case-based reasoning and disease-modelling approaches would allow to identify similar patient groups and classify endotypes. Realization of these potentials is, however, subject to several barriers, including legal and ethical prerequisites of data access, syntactic and semantic integration, comparability of items and user-centered presentation. The German Medical Informatics Initiative is presented as a current undertaking that strives to address these issues by establishing a national infrastructure for the secondary use of routine clinical data.
CRIS-Team Scopus Importer:2021-11-16
Keywords: electronic patient record; secondary use; systems integration

Digital Support to Multimodal Community-Based Prehabilitation: Looking for Optimization of Health Value Generation - Barberan-Garcia, Anael; Cano, Isaac; Bongers, Bart C.; Seyfried, Steffen; Ganslandt, Thomas; Herrle, Florian; Martinez-Palli, Graciela (Frontiers in Oncology, 2021)
Prehabilitation has shown its potential for most intra-cavity surgery patients on enhancing preoperative functional capacity and postoperative outcomes. However, its large-scale implementation is limited by several constrictions, such as: i) unsolved practicalities of the service workflow, ii) challenges associated to change management in collaborative care; iii) insufficient access to prehabilitation; iv) relevant percentage of program drop-outs; v) need for program personalization; and, vi) economical sustainability. Transferability of prehabilitation programs from the hospital setting to the community would potentially provide a new scenario with greater accessibility, as well as offer an opportunity to effectively address the aforementioned issues and, thus, optimize healthcare value generation. A core aspect to take into account for an optimal management of prehabilitation programs is to use proper technological tools enabling: i) customizable and interoperable integrated care pathways facilitating personalization of the service and effective engagement among stakeholders; ii) remote monitoring (i.e. physical activity, physiological signs and patient-reported outcomes and experience measures) to support patient adherence to the program and empowerment for self-management; and, iii) use of health risk assessment supporting decision making for personalized service selection. The current manuscript details a proposal to bring digital innovation to community-based prehabilitation programs. Moreover, this approach has the potential to be adopted by programs supporting long-term management of cancer patients, chronic patients and prevention of multimorbidity in subjects at risk.
CRIS-Team Scopus Importer:2021-11-16
Keywords: behavioral change; eHealth; exercise training and nutrition counseling; mHealth; physical activity; prehabilitation; psychological well-being; technology – ICT

Effekte von Peer Teaching in einem geburtshilflich-gynäkologischen Praktikum - Frobenius, Wolfgang; Ganslandt, Thomas; Juenger, J.; Beckmann, Matthias; Cupisti, S. (Geburtshilfe und Frauenheilkunde, 2009)
Obstetrics gynecology medical education peer teaching block course OSCE Background: Due to limited staffing resources and the need to preserve patients' privacy, it is difficult to improve the assessed practical courses in gynecology required by law for the licensing of physicians in Germany (Ärztliche Approbations- ordnung, ÄAppO). A previously published project showed that including structured practical teaching (skills training) using suitable obstetric and gynecological models in courses taught by specially trained student teachers (peer teachers) represents a potential solution to the problem. However, there are as yet no data regarding the effectiveness of this approach. Aim: Aim of the study was to determine the effectiveness of a peer-assisted skills training for students as part of a gynecological and obstetric practical course for theoretical and practical examinations (objective structured clinical examination, mini-OSCE) (primary end point). How do students who have been taught in this way rate the educational quality, teaching atmosphere, personal learning progress, and this type of practical course overall (secondary end points)? Methods: Following the implementation of a skills training section during a practical course, students (n = 139) were randomly assigned to 24 groups. Most groups consisted of six participants. Half of these groups, with a total of 71 students, received peer teaching during skills training, while the other half (n = 68) were taught by postgraduate lecturers. The rest of the one-week practical course, including the theoretical and practical examinations, was identical for all groups. The results of a theoretical and practical examination, also newly implemented (primary end point) and the results of the evaluation before (comparability of the groups) and after the practical course (secondary end points) were analyzed. Results: The groups taught by postgraduate lecturers did not differ with regard to age, sex, num die Ergebnisse der Evaluation vor (Vergleichbarkeit der Gruppen) und nach dem Praktikum (sekundare Endpunkte). Ergebnisse: Die von postgraduierten Dozenten unterrichteten Gruppen unterschieden sich hinsichtlich Alter, Geschlecht, Semesterzahl und fachspezifischer Vorbildung nicht von den durch Peer Teacher geschulten Studierenden. An den 3 überwiegend praktischen OSKE-Stationen erzielten die Studierenden der Post- graduierten-Gruppe 58,26±3,2 (95%-KI 57,5-59,5) von 62 Punkten, in der Peer-Teacher-Gruppe waren es 58,82±2,6 (95%-KI 58,2-59,4) (p = 0,27). Bei der Bearbeitung der Stationen mit Fall- darstellungen, fur die beiden Gruppen in Seminaren von postgra- duierten Dozenten vermittelter Stoff relevant war, erreichte die Postgraduierten-Gruppe 26,52±3,2 (95%-KI 25,7-27,3) von 30 Punkten, in der Peer-Teacher-Gruppe waren es 26,29 ±2,7 (95%-KI 25,6-27,0) (p = 0,64). Auch in der abschließenden Evaluation hinsichtlich Motivation der Ausbilder, Lernklima sowie Effektivität der praktischen Prüfungsvorbereitung gab es keine signifikanten Unterschiede. Die postgraduierten Dozenten schnitten lediglich bei der Vermittlung des theoretischen Hintergrunds und der Beantwortung von Fragen signifikant besser ab als die Peer Teacher. Schlussfolgerung: Zur Vermittlung der Prinzipien grundlegender praktischer ärztlicher Fertigkeiten in einem frauenheilkund- lichen Blockpraktikum ist ein Teaching durch speziell geschulte Peers ebenso effektiv wie der Unterricht durch postgraduierte Dozenten. ber of completed semesters, or prior specialist training from the students who were taught by the peer teachers. In the purely practical OSCE stations, the students in the postgraduate group achieved 58.26 ±3.2 (95 % CI, 57.5-59.5) out of a total of 62 points, while in the peer teacher group they scored 58.82 ±2.6 (95% CI, 58.2-59.4) (p = 0.27). In dealing with the case descriptions, for which material taught to both groups in seminars by postgraduate lecturers was relevant, the postgraduate group achieved 26.52±3.2 (95% CI, 25.7-27.3) of 30 points, while in the peer teacher group the figure was 26.29 ±2.7 (95% CI, 25.6-27.0) (p = 0.64). In the final evaluation regarding the motivation of the trainers, the teaching atmosphere, and the effectiveness of preparation for the practical examination, no significant differences were found. The postgraduate lecturers only had significantly better results than the peer teachers when conveying the theoretical background and responding to questions. Conclusions: To communicate the principles of basic practical medical skills in a gynecological practical course, teaching by specially trained peers is just as effective as teaching by postgraduate teachers. © Georg Thieme Verlag KG Stuttgart.
CRIS-Team Scopus Importer:2021-11-16

Electronic Support for Retrospective Analysis in the Field of Radiation Oncology: Proof of Principle Using an Example of Fractionated Stereotactic Radiotherapy of 251 Meningioma Patients - Rutzner, Sandra; Fietkau, Rainer; Ganslandt, Thomas; Prokosch, Hans-Ulrich; Lubgan, Dorota (Frontiers in Oncology, 2017)
The purpose of this study is to verify the possible benefit of a clinical data warehouse (DWH) for retrospective analysis in the field of radiation oncology.We manually and electronically (using DWH) evaluated demographic, radiotherapy, and outcome data from 251 meningioma patients, who were irradiated from January 2002 to January 2015 at the Department of Radiation Oncology of the Erlangen University Hospital. Furthermore, we linked the Oncology Information System (OIS) MOSAIQ(®) to the DWH in order to gain access to irradiation data. We compared the manual and electronic data retrieval method in terms of congruence of data, corresponding time, and personal requirements (physician, physicist, scientific associate).The electronically supported data retrieval (DWH) showed an average of 93.9% correct data and significantly (p = 0.009) better result compared to manual data retrieval (91.2%). Utilizing a DWH enables the user to replace large amounts of manual activities (668 h), offers the ability to significantly reduce data collection time and labor demand (35 h), while simultaneously improving data quality. In our case, work time for manually data retrieval was 637 h for the scientific assistant, 26 h for the medical physicist, and 5 h for the physician (total 668 h).Our study shows that a DWH is particularly useful for retrospective analysis in the radiation oncology field. Routine clinical data for a large patient group can be provided ready for analysis to the scientist and data collection time can be significantly reduced. Furthermore, linking multiple data sources in a DWH offers the ability to improve data quality for retrospective analysis, and future research can be simplified.
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Empowering personalized oncology: evolution of digital support and visualization tools for molecular tumor boards. - Strantz, Cosima; Böhm, Dominik; Ganslandt, Thomas; Börries, Melanie; Metzger, Patrick; Pauli, Thomas; Blaumeiser, Andreas; Scheiter, Alexander; Jung, Ian Christopher; Christoph, Jan; Manuilova, Iryna; Strauch, Konstantin; et al. (BMC Medical Informatics and Decision Making, 2025)
BACKGROUND: Molecular tumor boards (MTBs) play a pivotal role in personalized oncology, leveraging complex data sets to tailor therapy for cancer patients. The integration of digital support and visualization tools is essential in this rapidly evolving field facing fast-growing data and changing clinical processes. This study addresses the gap in understanding the evolution of software and visualization needs within MTBs and evaluates the current state of digital support. Alignment between user requirements and software development is crucial to avoid waste of resources and maintain trust. METHODS: In two consecutive nationwide medical informatics projects in Germany, surveys and expert interviews were conducted as stage 1 (n = 14), stage 2 (n = 30), and stage 3 (n = 9). Surveys, via the SoSci Survey tool, covered participants' roles, working methods, and support needs. The second survey additionally addressed requirements for visualization solutions in molecular tumor boards. These aimed to understand diverse requirements for preparation, implementation, and documentation. Nine semi-structured expert interviews complemented quantitative findings through open discussion. RESULTS: Using quantitative and qualitative analyses, we show that existing digital tools may improve therapy recommendations and streamline MTB case preparation, while continuous training and system improvements are needed. CONCLUSIONS: Our study contributes to the field by highlighting the importance of developing user-centric, customizable software solutions that can adapt to the fast-paced environment of MTBs to advance personalized oncology. In doing so, it lays the foundation for further advances in personalized medicine in oncology and points to a shift towards more efficient, technology-driven clinical decision-making processes. This research not only enriches our understanding of the integration of digital tools into MTBs, but also signals a broader shift towards technological innovation in healthcare.
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Evaluating and Enhancing the Fitness-for-Purpose of Electronic Health Record Data: Qualitative Study on Current Practices and Pathway to an Automated Approach Within the Medical Informatics for Research and Care in University Medicine Consortium - Wabo, Gaetan Kamdje; Moorthy, Preetha; Siegel, Fabian; Seuchter, Susanne A.; Ganslandt, Thomas (JMIR Medical Informatics, 2024)
Background: Leveraging electronic health record (EHR) data for clinical or research purposes heavily depends on data fitness. However, there is a lack of standardized frameworks to evaluate EHR data suitability, leading to inconsistent quality in data use projects (DUPs). This research focuses on the Medical Informatics for Research and Care in University Medicine (MIRACUM) Data Integration Centers (DICs) and examines empirical practices on assessing and automating the fitness-for-purpose of clinical data in German DIC settings. Objective: The study aims (1) to capture and discuss how MIRACUM DICs evaluate and enhance the fitness-for-purpose of observational health care data and examine the alignment with existing recommendations and (2) to identify the requirements for designing and implementing a computer-assisted solution to evaluate EHR data fitness within MIRACUM DICs. Methods: A qualitative approach was followed using an open-ended survey across DICs of 10 German university hospitals affiliated with MIRACUM. Data were analyzed using thematic analysis following an inductive qualitative method. Results: All 10 MIRACUM DICs participated, with 17 participants revealing various approaches to assessing data fitness, including the 4-eyes principle and data consistency checks such as cross-system data value comparison. Common practices included a DUP-related feedback loop on data fitness and using self-designed dashboards for monitoring. Most experts had a computer science background and a master’s degree, suggesting strong technological proficiency but potentially lacking clinical or statistical expertise. Nine key requirements for a computer-assisted solution were identified, including flexibility, understandability, extendibility, and practicability. Participants used heterogeneous data repositories for evaluating data quality criteria and practical strategies to communicate with research and clinical teams. Conclusions: The study identifies gaps between current practices in MIRACUM DICs and existing recommendations, offering insights into the complexities of assessing and reporting clinical data fitness. Additionally, a tripartite modular framework for fitness-for-purpose assessment was introduced to streamline the forthcoming implementation. It provides valuable input for developing and integrating an automated solution across multiple locations. This may include statistical comparisons to advanced machine learning algorithms for operationalizing frameworks such as the 3×3 data quality assessment framework. These findings provide foundational evidence for future design and implementation studies to enhance data quality assessments for specific DUPs in observational health care settings.
CRIS-Team Scopus Importer:2024-09-13
Keywords: data integration center; data quality; data science; data use; EHR; EHR data; EHRs; electronic health record; fitness-for-purpose; integration; medical informatics; Medical Informatics for Research and Care in University Medicine; Medical Informatics Initiative; MIRACUM consortium; record; records; secondary use; survey; surveys; thematic analysis; visualization; visualizations

Experiences of Transforming a Complex Nephrologic Care and Research Database into i2b2 Using the IDRT Tools - Maier, Christian; Christoph, Jan; Schmidt, Danilo; Ganslandt, Thomas; Prokosch, Hans-Ulrich; Kraus, Stefan; Sedlmayr, Martin (Journal of Healthcare Engineering, 2019)
The secondary use of data from electronic medical records has become an important factor to determine and to identify various causes of disease. For this reason, applications like informatics for integrating biology and the bedside (i2b2) offer a GUI-based front end to select patient cohorts. To make use of those tools, however, clinical data need to be extracted from the Electronic Health Record (EHR) system and integrated into the data schema of i2b2. We used TBase, a documentation system for nephrologic transplantations, as a source system and applied the Integrated Data Repository Toolkit (IDRT) for the Extract, Transform, and Load (ETL) process to load the data into i2b2. Since i2b2 uses an entity-attribute-value (EAV) schema, which is a fundamentally different way of modeling data in comparison to a standard relational schema in TBase, we evaluated if (a) the data relationship of the source system entities can still be represented in the i2b2 schema and if (b) the IDRT is a suitable solution for loading the data of a comprehensive data schema like TBase into i2b2. For that reason, we identified entities in the TBase data schema which were relevant for answering questions on cohort identification. By doing so, we found out that the entities had different structures that needed to be handled differently for the ETL process. Furthermore, the use of IDRT revealed shortcomings with regard to large input data and specific data structures that are part of most modern EHR systems. However, this project also showed that our way of modeling the TBase data in i2b2 has been proven to be successful in terms of answering the most common questions of clinicians on cohort identification.
CRIS-Team WoS Importer:2019-03-06

Feasibility analysis of conducting observational studies with the electronic health record - von Lucadou, Marcel; Ganslandt, Thomas; Prokosch, Hans-Ulrich; Toddenroth, Dennis (BMC Medical Informatics and Decision Making, 2019)
BACKGROUND: The secondary use of electronic health records (EHRs) promises to facilitate medical research. We reviewed general data requirements in observational studies and analyzed the feasibility of conducting observational studies with structured EHR data, in particular diagnosis and procedure codes. METHODS: After reviewing published observational studies from the University Hospital of Erlangen for general data requirements, we identified three different study populations for the feasibility analysis with eligibility criteria from three exemplary observational studies. For each study population, we evaluated the availability of relevant patient characteristics in our EHR, including outcome and exposure variables. To assess data quality, we computed distributions of relevant patient characteristics from the available structured EHR data and compared them to those of the original studies. We implemented computed phenotypes for patient characteristics where necessary. In random samples, we evaluated how well structured patient characteristics agreed with a gold standard from manually interpreted free texts. We categorized our findings using the four data quality dimensions "completeness", "correctness", "currency" and "granularity". RESULTS: Reviewing general data requirements, we found that some investigators supplement routine data with questionnaires, interviews and follow-up examinations. We included 847 subjects in the feasibility analysis (Study 1 n = 411, Study 2 n = 423, Study 3 n = 13). All eligibility criteria from two studies were available in structured data, while one study required computed phenotypes in eligibility criteria. In one study, we found that all necessary patient characteristics were documented at least once in either structured or unstructured data. In another study, all exposure and outcome variables were available in structured data, while in the other one unstructured data had to be consulted. The comparison of patient characteristics distributions, as computed from structured data, with those from the original study yielded similar distributions as well as indications of underreporting. We observed violations in all four data quality dimensions. CONCLUSIONS: While we found relevant patient characteristics available in structured EHR data, data quality problems may entail that it remains a case-by-case decision whether diagnosis and procedure codes are sufficient to underpin observational studies. Free-text data or subsequently supplementary study data may be important to complement a comprehensive patient history.
CRIS-Team Scopus Importer:2019-11-08
Keywords: Availability; Completeness; Correctness; Currency; Data quality; Electronic health record; Granularity; Observational study; Retrospective study

Feasibility and clinical applications of multiple breath wash-out (MBW) testing using sulphur hexafluoride in adults with bronchial asthma - Trinkmann, Frederik; Lenz, Steffi A.; Schafer, Julia; Gawlitza, Joshua; Schroeter, Michele; Gradinger, Tobias; Akin, Ibrahim; Borggrefe, Martin; Ganslandt, Thomas; Saur, Joachim (Scientific Reports, 2020)
Ventilation heterogeneity is frequent in bronchial asthma and can be assessed using multiple breath wash-out testing (MBW). Most data is available in paediatric patients and using nitrogen as a tracer gas. We aimed to evaluate sulphur hexafluoride (SF6) MBW in adult asthmatics. Spirometry, whole-body plethysmography, impulse oscillometry and SF6-MBW were prospectively performed. MBW parameters reflecting global (lung clearance index, LCI), acinar (Sacin) and conductive (Scond) ventilation heterogeneity were derived from three consecutive wash-outs. LCI was calculated for the traditional 2.5% and an earlier 5% stopping point that has the potential to reduce wash-out times. 91 asthmatics (66%) and 47 non-asthmatic controls (34%) were included in final analysis. LCI2.5 and LCI5 were higher in asthmatics (p < 0.001). Likewise, Sacin and Scond were elevated (p < 0.001 and p < 0.01). Coefficient of variation was 3.4% for LCI2.5 and 3.5% for LCI5 in asthmatics. Forty-one asthmatic patients had normal spirometry. ROC analysis revealed an AUC of 0.906 for the differentiation from non-asthmatic controls exceeding diagnostic performance of individual and conventional parameters (AUC = 0.819, p < 0.05). SF6-MBW is feasible and reproducible in adult asthmatics. Ventilation heterogeneity is increased as compared to non-asthmatic controls persisting in asthmatic patients with normal spirometry. Diagnostic performance is not affected using an earlier LCI stopping point while reducing wash-out duration considerably.
CRIS-Team Scopus Importer:2021-11-16

Finding the needle in the hay stack: An open architecture to support diagnosis of undiagnosed patients - Schaaf, Jannik; Boeker, Martin; Ganslandt, Thomas; Haverkamp, Christian; Hermann, Tim; Kadioglu, Dennis; Prokosch, Hans-Ulrich; Wagner, Thomas O.F.; Von Wagner, Michael; Schaefer, Johanna; Sedlmayr, Martin; Storf, Holger (Studies in Health Technology and Informatics, 2019)
Clinical Decision Support Systems (CDSS) are promising to support physicians in finding the right diagnosis of patients with rare diseases (RD). The MIRACUM consortium, which includes ten university hospitals in Germany, will establish a diagnosis support system for RD. This system conducts a similarity analysis on distributed clinical data with the aim to identify similar patient cases at each MIRACUM site to offer the physician a hint to a possible diagnosis.
CRIS-Team Scopus Importer:2019-09-20
Keywords: Clinical Decision Support; Data Science; Rare Diseases

Generation of Reliable Red Cell Concentrate Requirement Lists for Operations and Interventions from Data Warehouse Data and Patient-Related Batch Documentation of Blood Components - Zimmermann, Robert; Becker, Andreas; Hackstein, Holger; Ganslandt, Thomas (Transfusionsmedizin, 2021)
Failure to perform transfusion-preparatory diagnostics and to order a sufficient number of blood components prior to surgery is increasingly leading to clinical emergencies and avoidable risks for patients. The mandatory preparation of blood requirement lists often fails due to the availability of suitable data. The University Hospital Erlangen is a tertiary care hospital with 1400 beds. Since 2010, all relevant data for the interpretation of patterns in the use of blood components have been continuously collected here. We are able to integrate the clinical data warehouse components of the personal data of red blood cell (RBC) recipients, the codes of inpatients in the German Diagnosis Related Group (G-DRG) system, ICD codes, OPS codes and the data of the RBC components from the blood bank's IT system. The obtained DRGs, ICD codes and OPS codes are linked to the data of blood component consumption on an individual basis. Analyses can be carried out for the entire hospital as well as for specific departments. Ongoing refinement of the database queries improves the ability to correctly estimate blood requirements for standard surgery and interventions. The results differ considerably from the surgeons' estimates of the probability of a transfusion, i.e. the probability of a transfusion is often underestimated. Correct compliance with the 10 percent transfusion probability limit for the question of early preoperative provision of cross blood can be improved by such queries. Counteracting the phenomenon of "delayed transfusion" is becoming an increasingly important task for the clinically active transfusion medicine specialist. Creating statistics on the probability of transfusion by linking data warehouse data and batch documentation data from blood banks can provide the necessary tools.
CRIS-Team WoS Importer:2021-03-26

Germany's Approach for the Secondary Use of Federated Real-World Data in the German Portal for Medical Research Data - Kleinert, Philip; Gebhardt, Marie; Buckow, Karoline; Gründner, Julian; Ganslandt, Thomas; Prokosch, Hans-Ulrich; Semler, Sebastian C. (Studies in Health Technology and Informatics, 2024)
In the light of big data driven clinical research, fair access to real world clinical health data enables evidence to improve patient care. Germany's healthcare system provides an abundant data resource but unique challenges due to its federated nature, heterogeneity and high data-protection standards. The Medical Informatics Initiative (MII) developed concepts that are being implemented in the German Portal for Medical Research Data (FDPG) to grant access to distributed data-sources across state borders. The portal currently provides access to more than 10 million patient resources containing hundreds of millions of laboratory parameters, diagnostic reports, administered medications, procedures and specimens. Upcoming datasets include among others oncological data, molecular analysis results and microbiological findings. Here, we describe the philosophy, implementation and experience behind the framework: standardized access processes, interoperable fair data, software for in depth feasibility requests, tools to support researchers and hospital stakeholders alike as well as transparency measures to provide data use information for patients. Challenges remain to improve data quality and automatization of technical and organizational processes.
CRIS-Team Scopus Importer:2024-10-25
Keywords: Clinical Research Informatics; Federated IT Infrastructure; Real-World-Data; Secondary Use

High-dose subconjunctival cyclosporine a implants do not affect corneal neovascularization after high-risk keratoplasty - Bock, Felix; Matthaei, Mario; Reinhard, Thomas; Böhringer, Daniel; Christoph, Jan; Ganslandt, Thomas; Cursiefen, Claus (Ophthalmology, 2014)
To test whether subconjunctival cyclosporine A (CsA) implants affect the incidence and the degree of corneal neovascularization occurring after penetrating keratoplasty.Prospective, randomized, multicenter, controlled phase 2/3 clinical trial. The study comprised 43 trial sites in Germany, India, and the United States.Enrolled patients (n = 97) were randomized to 1 of 3 groups: treatment group A (n = 36), treatment group B (n = 40), and the control group (n = 21).Patients from each group received either of 2 doses of subconjunctival CsA (group A, low-dose CsA; group B, high-dose CsA) or placebo (carrier only) implants at the time of high-risk penetrating keratoplasty.The incidence and degree of corneal neovascularization occurring after penetrating keratoplasty were evaluated in a substudy (LX201-01 study: NCT00447187). A web-based image upload system was developed. Standardized digital slit-lamp pictures were quantitatively and objectively evaluated using Cell^F morphometry software.No statistically significant difference in incidence and degree of corneal neovascularization developing after penetrating keratoplasty was found between treatment groups and placebo group. Mean corneal neovascularization area at week 52 (visit 12) was 2.32±1.79% in treatment group A versus placebo (2.79±2.11%; P = 0.45) and 2.74±2.22% in treatment group B versus placebo (2.79±2.11%; P = 0.94).High-dose subconjunctival CsA implants do not significantly affect corneal neovascularization after high-risk penetrating keratoplasty. This suggests that local CsA has negligible antiangiogenic effects in the human cornea, at least in the transplant setting.
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Identitätsmanagement für Patienten in medizinischen Forschungsverbünden - Pommerening, Klaus; Helbing, Krister; Ganslandt, Thomas; Drepper, Johannes (Lecture Notes in Informatics (LNI), Proceedings - Series of the Gesellschaft fur Informatik (GI), 2012)
CRIS-Team Scopus Importer:2021-11-16

Implementation and Feasibility of Electronic Patient-Reported Outcome (ePRO) Data Entry in the PRAEGNANT Real-Time Advanced and Metastatic Breast Cancer Registry - Wallwiener, Markus; Heindl, Felix; Brucker, Sara Y.; Taran, Florin-Andrei; Hartkopf, Andreas; Overkamp, Friedrich; Kolberg, Hans-Christian; Hadji, Peyman; Tesch, Hans; Ettl, Johannes; Lux, Michael P.; Rauh, Claudia; et al. (Geburtshilfe und Frauenheilkunde, 2017)
Patient-reported outcomes (PROs) have been incorporated into clinical trials for many symptoms and medical conditions. A transition from paper-based capture of PROs to electronic PROs (ePROs) has recently started. This study reports on the feasibility of ePRO assessment in a prospective registry including molecular data for patients with advanced breast cancer.As part of the PRAEGNANT network, patients were invited by clinical trial staff, physicians, and nurses to complete three standardized Internet-based questionnaires (EQ 5D 5 L, CES-D and IPAQ). Feasibility was assessed by the staff members who assigned the user accounts by the patients. The completeness of the questionnaires was also assessed.Fifteen of 17 patients who were asked agreed to participate to complete the PRO questionnaires (EQ-5D-5L and CES-D). However, the IPAQ (physical activity) questionnaire was only validly completed by 9 patients. Feasibility was ranked better by the physicians and dedicated clinical trial staff than by the nursing staff.Incorporating ePRO questionnaires into an advanced breast cancer registry is feasible, and no major hurdles were reported. Involving stakeholders from the start, the application is tailored to the capacities and abilities of both patients and clinical staff. The patients' compliance was better with some questionnaires, but others may present difficulties.
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Implementation of a patient data management system : Effects on intensive care documentation - Castellanos, Ixchel; Ganslandt, Thomas; Prokosch, Hans-Ulrich; Schüttler, Jürgen; Bürkle, Thomas (Anaesthesist, 2013)
Patient data management systems (PDMS) enable digital documentation on intensive care units (ICU) and have positive effects on completness, quality and quantity of documented information. A commercial PDMS was implemented in a 25-bed ICU replacing paper-based patient charting. The ICU electronic patient record is completely managed inside the PDMS. IT compiles data from vital signs monitors, ventilators and further medical devices and facilitates some drug dose and fluid balance calculations as well as data reuse for administrative purposes. Ventilation time and patient severity scoring as well as coding of diagnoses and procedures is supported. Billing data transferred via interface to the central billing system of the hospital. Such benefits should show in measurable parameters, such as documented ventilator time, number of coded diagnoses and procedures and others. These parameters influence reimbursement in the German DRG system. Therefore, measurable changes in cost and reimbursement data of the ICU were expected.A retrospective analysis of documentation quality parameters, cost data and mortality rate of a 25-bed surgical ICU within a German university hospital 3 years before (2004-2006) and 5 years after (2007-2011) PDMS implementation. Selected parameters were documented electronically, consistently and reproducibly for the complete time span of 8 years including those years where no electronic patient recording was available. The following parameters were included: number of cleared DRG, cleared ventilator time, case mix (CM), case mix index (CMI), length of stay, number of coded diagnoses and procedures, detailed overview of a specific procedure code based on daily Apache II and TISS Core 10 scores, mortality, total ICU costs and revenues and partial profits for specific ICU procedures, such as renal replacement therapy and blood products.Systematic shifts were detected over the study period, such as increasing case numbers and decreasing length of stay as well as annual fluctuations in severity of disease seen in the CM and CMI. After PDMS introduction, the total number of coded diagnoses increased but the proportion of DRG relevant diagnoses dropped significantly. The number of procedures increased (not significantly) and the number of procedures per case did not rise significantly. The procedure 8-980 showed a significant increase after PDMS introduction whereas the DRG-relevant proportion of those procedures dropped insignificantly. The number of ventilator-associated DRG cases as well as the total ventilator time increased but not significantly. Costs and revenues increased slightly but profit varied considerably from year to year in the 5 years after system implementation. A small increase was observed per case, per nursing day and per case mix point. Additional revenues for specific ICU procedures increased in the years before and dropped after PDMS implementation. There was an insignificant increase in ICU mortality rate from 7.4 % in the year 2006 (before) to 8.5 % in 2007 (after PDMS implementation). In the following years mortality dropped below the base level.The implementation of the PDMS showed only small effects on documentation of reimbursement-relevant parameters which were too small to set off against the total investment. The method itself, a long-term follow-up of different parameters proved successful and can be adapted by other organizations. The quality of results depends on the availability of long-term parameters in good quality. No significant influence of PDMS on mortality was found.
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Implementation of Task-Tracking Software for Clinical IT Management - Purohit, Anne-Maria; Brutscheck, Clemens; Prokosch, Hans-Ulrich; Ganslandt, Thomas; Schneider, Martin (Studies in health technology and informatics, 2017)
Often in clinical IT departments, many different methods and IT systems are used for task-tracking and project organization. Based on managers' personal preferences and knowledge about project management methods, tools differ from team to team and even from employee to employee. This causes communication problems, especially when tasks need to be done in cooperation with different teams. Monitoring tasks and resources becomes impossible: there are no defined deliverables, which prevents reliable deadlines. Because of these problems, we implemented task-tracking software which is now in use across all seven teams at the University Hospital Erlangen. Over a period of seven months, a working group defined types of tasks (project, routine task, etc.), workflows, and views to monitor the tasks of the 7 divisions, 20 teams and 340 different IT services. The software has been in use since December 2016.
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Keywords: Health information management; Information management; Project management

Indirect determination of hematology reference intervals in adult patients on Beckman Coulter UniCell DxH 800 and Abbott CELL-DYN Sapphire devices - Zierk, Jakob; Arzideh, Farhad; Haeckel, Rainer; Rauh, Manfred; Metzler, Markus; Ganslandt, Thomas; Krause, Stefan (Clinical Chemistry and Laboratory Medicine, 2018)
Background Conventional establishment of reference intervals for hematological analytes is challenging due to the need to recruit healthy persons. Indirect methods address this by deriving reference intervals from clinical laboratory databases which contain large datasets of both physiological and pathological test results. Methods We used the "Reference Limit Estimator" (RLE) to establish reference intervals for common hematology analytes in adults aged 18-60 years. One hundred and ninety-five samples from 44,519 patients, measured on two different devices in a tertiary care center were analyzed. We examined the influence of patient cohorts with an increasing proportion of abnormal test results, compared sample selection strategies, explored inter-device differences, and analyzed the stability of reference intervals in simulated datasets with varying overlap of pathological and physiological test results. Results Reference intervals for hemoglobin, hematocrit, red cell count and platelet count remained stable, even if large numbers of pathological samples were included. Reference intervals for red cell indices, red cell distribution width and leukocyte count were sufficiently stable, if patient cohorts with the highest fraction of pathological samples were excluded. In simulated datasets, estimated reference limits shifted, if the pathological dataset contributed more than 15%-20% of total samples and approximated the physiological distribution. Advanced sample selection techniques did not improve the algorithm's performance. Inter-device differences were small except for red cell distribution width. Conclusions The RLE is well-suited to create reference intervals from clinical laboratory databases even in the challenging setting of a adult tertiary care center. The procedure can be used as a complement for reference interval determination where conventional approaches are limited.
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Integrated Data Repository Toolkit (IDRT). A Suite of Programs to Facilitate Health Analytics on Heterogeneous Medical Data - Bauer, C. R. K. D.; Ganslandt, Thomas; Baum, B.; Christoph, Jan; Engel, Igor; Loebe, M.; Mate, Sebastian; Staeubert, S.; Drepper, J.; Prokosch, Hans-Ulrich; Winter, A.; Sax, Ulrich (Methods of Information in Medicine, 2016)
In recent years, research data warehouses moved increasingly into the focus of interest of medical research. Nevertheless, there are only a few center-independent infrastructure solutions available. They aim to provide a consolidated view on medical data from various sources such as clinical trials, electronic health records, epidemiological registries or longitudinal cohorts. The i2b2 framework is a well-established solution for such repositories, but it lacks support for importing and integrating clinical data and metadata.The goal of this project was to develop a platform for easy integration and administration of data from heterogeneous sources, to provide capabilities for linking them to medical terminologies and to allow for transforming and mapping of data streams for user-specific views.A suite of three tools has been developed: the i2b2 Wizard for simplifying administration of i2b2, the IDRT Import and Mapping Tool for loading clinical data from various formats like CSV, SQL, CDISC ODM or biobanks and the IDRT i2b2 Web Client Plugin for advanced export options. The Import and Mapping Tool also includes an ontology editor for rearranging and mapping patient data and structures as well as annotating clinical data with medical terminologies, primarily those used in Germany (ICD-10-GM, OPS, ICD-O, etc.).With the three tools functional, new i2b2-based research projects can be created, populated and customized to researcher's needs in a few hours. Amalgamating data and metadata from different databases can be managed easily. With regards to data privacy a pseudonymization service can be plugged in. Using common ontologies and reference terminologies rather than project-specific ones leads to a consistent understanding of the data semantics.i2b2's promise is to enable clinical researchers to devise and test new hypothesis even without a deep knowledge in statistical programing. The approach presented here has been tested in a number of scenarios with millions of observations and tens of thousands of patients. Initially mostly observant, trained researchers were able to construct new analyses on their own. Early feedback indicates that timely and extensive access to their "own" data is appreciated most, but it is also lowering the barrier for other tasks, for instance checking data quality and completeness (missing data, wrong coding).
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Integrated Data Repository Toolkit: Werkzeuge zur Nachnutzung medizinischer Daten für die Forschung - Ganslandt, Thomas; Sax, U.; Löbe, M.; Drepper, J.; Bauer, C.; Baum, B.; Christoph, Jan; Mate, Sebastian; Quade, M.; Stäubert, S.; Prokosch, Hans-Ulrich (Lecture Notes in Informatics (LNI), Proceedings - Series of the Gesellschaft fur Informatik (GI), 2012)
CRIS-Team Scopus Importer:2021-11-16

Integrating the Patient Perspective into Healthcare and Real-World Evidence: The Multi-site, Cross-Disease, Patient-Centered Outcomes Research Project in the Medical Informatics Initiative (PCOR-MII) - Rogge, A. A.; Mukowski-Kickhöfel, R.; Boeker, M.; Budde, K.; Debertshäuser, T.; Dugas, M.; Erim, Yesim; Friederich, H. C.; Ganslandt, Thomas; Giel, K.; Henningsen, P.; Herrmann, T.; et al. (Journal of Healthcare Informatics Research, 2025)
This paper presents the Patient-Centered Outcomes Research within the Medical Informatics Initiative (PCOR-MII) project, focusing on the integration of patient-reported outcomes (PROs) into a large-scale national data sharing infrastructure, established in Germany by the Medical Informatics Initiative (MII). PCOR-MII aims to systematically address the interests of various stakeholders in patient-reported health data and three dimensions of clinical utility: (1) prediction, (2) monitoring, and (3) outcome assessment. The project builds upon harmonized technical, data, and compliance environments established at the participating institutions as part of the MII to deploy and roll out software solutions for capturing PROs and making them accessible within local electronic health record (EHR) systems. To overcome interoperability challenges, PCOR-MII is developing a construct-oriented PROM module for the Health Level 7 (HL7) Fast Healthcare Interoperability Resources (FHIR)–based German National Core Dataset. The project applies its approach to three patient populations with distinct characteristics: anorexia nervosa targeting risk prediction (dimension 1), kidney transplantation prioritizing health status and adherence monitoring (dimension 2), and persistent somatic symptoms primarily aimed at assessing and understanding outcomes (dimension 3). With their emphasis on different aspects of PROs, those application areas can serve as blueprints for a broader roll-out. PCOR-MII represents a structured and comprehensive effort to incorporate PROs into a national data infrastructure, promising more precise diagnostics, improved treatment decisions, and the generation of new biomedical insights. We believe that our structured approach may serve as a guiding framework for others aiming to implement PROs in diverse healthcare settings.
CRIS-Team Scopus Importer:2025-02-17
Keywords: Chronic disease management; Health data integration; Monitoring treatment; Patient-centered care; Patient-reported outcomes; Real-world evidence

IT infrastructure components to support clinical care and translational research projects in a comprehensive cancer center - Prokosch, Hans-Ulrich; Ries, Markus; Beyer, Alexander; Schwenk, Martin; Seggewies, Christof; Köpcke, Felix; Mate, Sebastian; Martin, Marcus; Bärthlein, Barbara; Beckmann, Matthias; Stürzl, Michael; Croner, Roland; et al. (User Centred Networked Health Care, 2011)
This paper presents the concept of an integrated IT infrastructure framework established at the comprehensive cancer center at the University Hospital Erlangen. The framework is based on the single source concept where data from the electronic medical record are reused for clinical and translational research projects. The applicability of the approach is illustrated by two case studies from colon cancer and prostate cancer research projects. © 2011 European Federation for Medical Informatics. All rights reserved.
UnivIS-Import:2015-04-14:Pub.2011.tech.IMMD.infome.itinfr
Keywords: Cancer documentation; Center; Comprehensive cancer; IT infrastructure framework; Single source concept; Translational research

Lesion-conditioning of synthetic MRI-derived subtraction-MIPs of the breast using a latent diffusion model - Kapsner, Lorenz; Folle, Lukas; Hadler, Dominique; Eberle, Jessica; Balbach, Eva; Liebert, Andrzej; Ganslandt, Thomas; Wenkel, Evelyn; Ohlmeyer, Sabine; Uder, Michael; Bickelhaupt, Sebastian (Scientific Reports, 2024)
The purpose of this feasibility study is to investigate if latent diffusion models (LDMs) are capable to generate contrast enhanced (CE) MRI-derived subtraction maximum intensity projections (MIPs) of the breast, which are conditioned by lesions. We trained an LDM with n = 2832 CE-MIPs of breast MRI examinations of n = 1966 patients (median age: 50 years) acquired between the years 2015 and 2020. The LDM was subsequently conditioned with n = 756 segmented lesions from n = 407 examinations, indicating their location and BI-RADS scores. By applying the LDM, synthetic images were generated from the segmentations of an independent validation dataset. Lesions, anatomical correctness, and realistic impression of synthetic and real MIP images were further assessed in a multi-rater study with five independent raters, each evaluating n = 204 MIPs (50% real/50% synthetic images). The detection of synthetic MIPs by the raters was akin to random guessing with an AUC of 0.58. Interrater reliability of the lesion assessment was high both for real (Kendall’s W = 0.77) and synthetic images (W = 0.85). A higher AUC was observed for the detection of suspicious lesions (BI-RADS ≥ 4) in synthetic MIPs (0.88 vs. 0.77; p = 0.051). Our results show that LDMs can generate lesion-conditioned MRI-derived CE subtraction MIPs of the breast, however, they also indicate that the LDM tended to generate rather typical or ‘textbook representations’ of lesions.
CRIS-Team Scopus Importer:2024-03-29

Leveraging Real-World Data for the Selection of Relevant Eligibility Criteria for the Implementation of Electronic Recruitment Support in Clinical Trials - Melzer, Georg; Maiwald, Tim; Prokosch, Hans-Ulrich; Ganslandt, Thomas (Applied Clinical Informatics, 2021)
Background Even though clinical trials are indispensable for medical research, they are frequently impaired by delayed or incomplete patient recruitment, resulting in cost overruns or aborted studies. Study protocols based on real-world data with precisely expressed eligibility criteria and realistic cohort estimations are crucial for successful study execution. The increasing availability of routine clinical data in electronic health records (EHRs) provides the opportunity to also support patient recruitment during the prescreening phase. While solutions for electronic recruitment support have been published, to our knowledge, no method for the prioritization of eligibility criteria in this context has been explored. Methods In the context of the Electronic Health Records for Clinical Research (EHR4CR) project, we examined the eligibility criteria of the KATHERINE trial. Criteria were extracted from the study protocol, deduplicated, and decomposed. A paper chart review and data warehouse query were executed to retrieve clinical data for the resulting set of simplified criteria separately from both sources. Criteria were scored according to disease specificity, data availability, and discriminatory power based on their content and the clinical dataset. Results The study protocol contained 35 eligibility criteria, which after simplification yielded 70 atomic criteria. For a cohort of 106 patients with breast cancer and neoadjuvant treatment, 47.9% of data elements were captured through paper chart review, with the data warehouse query yielding 26.9% of data elements. Score application resulted in a prioritized subset of 17 criteria, which yielded a sensitivity of 1.00 and specificity 0.57 on EHR data (paper charts, 1.00 and 0.80) compared with actual recruitment in the trial. Conclusion It is possible to prioritize clinical trial eligibility criteria based on real-world data to optimize prescreening of patients on a selected subset of relevant and available criteria and reduce implementation efforts for recruitment support. The performance could be further improved by increasing EHR data coverage.
CRIS-Team Scopus Importer:2021-01-29
Keywords: clinical trial; data warehousing and data marts; electronic health records and systems; recruitment; secondary use

Lightweight Visual Transformers Outperform Convolutional Neural Networks for Gram-Stained Image Classification: An Empirical Study - Kim, Hee E.; Maros, Mate E.; Miethke, Thomas; Kittel, Maximilian; Siegel, Fabian; Ganslandt, Thomas (Biomedicines, 2023)
We aimed to automate Gram-stain analysis to speed up the detection of bacterial strains in patients suffering from infections. We performed comparative analyses of visual transformers (VT) using various configurations including model size (small vs. large), training epochs (1 vs. 100), and quantization schemes (tensor- or channel-wise) using float32 or int8 on publicly available (DIBaS, n = 660) and locally compiled (n = 8500) datasets. Six VT models (BEiT, DeiT, MobileViT, PoolFormer, Swin and ViT) were evaluated and compared to two convolutional neural networks (CNN), ResNet and ConvNeXT. The overall overview of performances including accuracy, inference time and model size was also visualized. Frames per second (FPS) of small models consistently surpassed their large counterparts by a factor of 1-2×. DeiT small was the fastest VT in int8 configuration (6.0 FPS). In conclusion, VTs consistently outperformed CNNs for Gram-stain classification in most settings even on smaller datasets.
CRIS-Team Scopus Importer:2023-06-09
Keywords: classification; convolutional neural network; deep learning; Gram-stain analysis; quantization; vision transformer

Machine Learning Models of Post-Intubation Hypoxia During General Anesthesia - Sippl, Philipp; Ganslandt, Thomas; Prokosch, Hans-Ulrich; Münster, Tino; Toddenroth, Dennis (Studies in health technology and informatics, 2017)
Fine-meshed perioperative measurements are offering enormous potential for automatically investigating clinical complications during general anesthesia. In this study, we employed multiple machine learning methods to model perioperative hypoxia and compare their respective capabilities. After exporting and visualizing 620 series of perioperative vital signs, we had ten anesthesiologists annotate the subjective presence and severity of temporary post-intubation oxygen desaturation. We then applied specific clustering and prediction methods on the acquired annotations, and evaluated their performance in comparison to the inter-rater agreement between experts. When reproducing the expert annotations, the sensitivity and specificity of multi-layer neural networks substantially outperformed clustering and simpler threshold-based methods. The achieved performance of our best automated hypoxia models thereby approximately equaled the observed agreement between different medical experts. Furthermore, we deployed our classification methods for processing unlabeled inputs to estimate the incidence of hypoxic episodes in another sizeable patient cohort, which attests to the feasibility of using the approach on a larger scale. We interpret that our machine learning models could be instrumental for computerized observational studies of the clinical determinants of post-intubation oxygen deficiency. Future research might also investigate potential benefits of more advanced preprocessing approaches such as automated feature learning.
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Medizinische Datenbanken - Lenz, Richard; Ganslandt, Thomas (Biomedizinische Technik - Band 6: Medizinische Informatik, 2013)
UnivIS-Import:2015-04-20:Pub.2013.tech.IMMD.IMMD6.medizi

MIRACUM: Medical Informatics in Research and Care in University Medicine - Prokosch, Hans-Ulrich; Acker, Till; Bernarding, Johannes; Binder, Harald; Boeker, Martin; Boerries, Melanie; Daumke, Philipp; Ganslandt, Thomas; Hesser, Juergen; Hoening, Gunther; Neumaier, Michael; Marquardt, Kurt; et al. (Methods of Information in Medicine, 2018)
INTRODUCTION: This article is part of the Focus Theme of Methods of Information in Medicine on the German Medical Informatics Initiative. Similar to other large international data sharing networks (e.g. OHDSI, PCORnet, eMerge, RD-Connect) MIRACUM is a consortium of academic and hospital partners as well as one industrial partner in eight German cities which have joined forces to create interoperable data integration centres (DIC) and make data within those DIC available for innovative new IT solutions in patient care and medical research. OBJECTIVES: Sharing data shall be supported by common interoperable tools and services, in order to leverage the power of such data for biomedical discovery and moving towards a learning health system. This paper aims at illustrating the major building blocks and concepts which MIRACUM will apply to achieve this goal. GOVERNANCE AND POLICIES: Besides establishing an efficient governance structure within the MIRACUM consortium (based on the steering board, a central administrative office, the general MIRACUM assembly, six working groups and the international scientific advisory board), defining DIC governance rules and data sharing policies, as well as establishing (at each MIRACUM DIC site, but also for MIRACUM in total) use and access committees are major building blocks for the success of such an endeavor. ARCHITECTURAL FRAMEWORK AND METHODOLOGY: The MIRACUM DIC architecture builds on a comprehensive ecosystem of reusable open source tools (MIRACOLIX), which are linkable and interoperable amongst each other, but also with the existing software environment of the MIRACUM hospitals. Efficient data protection measures, considering patient consent, data harmonization and a MIRACUM metadata repository as well as a common data model are major pillars of this framework. The methodological approach for shared data usage relies on a federated querying and analysis concept. USE CASES: MIRACUM aims at proving the value of their DIC with three use cases: IT support for patient recruitment into clinical trials, the development and routine care implementation of a clinico-molecular predictive knowledge tool, and molecular-guided therapy recommendations in molecular tumor boards. RESULTS: Based on the MIRACUM DIC release in the nine months conceptual phase first large scale analysis for stroke and colorectal cancer cohorts have been pursued. DISCUSSION: Beyond all technological challenges successfully applying the MIRACUM tools for the enrichment of our knowledge about diagnostic and therapeutic concepts, thus supporting the concept of a Learning Health System will be crucial for the acceptance and sustainability in the medical community and the MIRACUM university hospitals.
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Mitochondrial DNA copy number is associated with mortality and infections in a large cohort of patients with chronic kidney disease - Fazzini, Federica; Lamina, Claudia; Fendt, Liane; Schultheiss, Ulla T.; Kotsis, Fruzsina; Hicks, Andrew A.; Meiselbach, Heike; Weissensteiner, Hansi; Forer, Lukas; Krane, Vera; Eckardt, Kai-Uwe; Köttgen, Anna; et al. (Kidney International, 2019)
Damage of mitochondrial DNA (mtDNA) with reduction in copy number has been proposed as a biomarker for mitochondrial dysfunction and oxidative stress. Chronic kidney disease (CKD) is associated with increased mortality and risk of cardiovascular disease, but the underlying mechanisms remain incompletely understood. Here we investigated the prognostic role of mtDNA copy number for cause-specific mortality in 4812 patients from the German Chronic Kidney Disease study, an ongoing prospective observational national cohort study of patients with CKD stage G3 and A1-3 or G1-2 with overt proteinuria (A3) at enrollment. MtDNA was quantified in whole blood using a plasmid-normalized PCR-based assay. At baseline, 1235 patients had prevalent cardiovascular disease. These patients had a significantly lower mtDNA copy number than patients without cardiovascular disease (fully-adjusted model: odds ratio 1.03, 95% confidence interval [CI] 1.01-1.05 per 10 mtDNA copies decrease). After four years of follow-up, we observed a significant inverse association between mtDNA copy number and all-cause mortality, adjusted for kidney function and cardiovascular disease risk factors (hazard ratio 1.37, 95% CI 1.09-1.73 for quartile 1 compared to quartiles 2-4). When grouped by causes of death, estimates pointed in the same direction for all causes but in a fully-adjusted model decreased copy numbers were significantly lower only in infection-related death (hazard ratio 1.82, 95% CI 1.08-3.08). A similar association was observed for hospitalizations due to infections in 644 patients (hazard ratio 1.19, 95% CI 1.00-1.42 in the fully-adjusted model). Thus, our data support a role of mitochondrial dysfunction in increased cardiovascular disease and mortality risks as well as susceptibility to infections in patients with CKD.
CRIS-Team Scopus Importer:2019-07-02
Keywords: chronic kidney disease; infections; mitochondrial DNA copy number; mortality

Moving Towards an EHR Data Quality Framework: The MIRACUM Approach - Kapsner, Lorenz A.; Kampf, Marvin; Seuchter, Susanne A.; Kamdje-Wabo, Gaetan; Gradinger, Tobias; Ganslandt, Thomas; Mate, Sebastian; Gründner, Julian; Kraska, Detlef; Prokosch, Hans-Ulrich (German Medical Data Sciences: Shaping Change – Creative Solutions for Innovative Medicine, 2019)
INTRODUCTION: Data quality (DQ) is an important prerequisite for secondary use of electronic health record (EHR) data in clinical research, particularly with regards to progressing towards a learning health system, one of the MIRACUM consortium's goals. Following the successful integration of the i2b2 research data repository in MIRACUM, we present a standardized and generic DQ framework. STATE OF THE ART: Already established DQ evaluation methods do not cover all of MIRACUM's requirements. CONCEPT: A data quality analysis plan was developed to assess common data quality dimensions for demographic-, condition-, procedure- and department-related variables of MIRACUM's research data repository. IMPLEMENTATION: A data quality analysis (DQA) tool was developed using R scripts packaged in a Docker image with all the necessary dependencies and R libraries for easy distribution. It integrates with the i2b2 data repository at each MIRACUM site, executes an analysis on the data and generates a DQ report. LESSONS LEARNED: Our DQA tool brings the analysis to the data and thus meets the MIRACUM data protection requirements. It evaluates established DQ dimensions of data repositories in a standardized and easily distributable way. This analysis allowed us to reveal and revise inconsistencies in earlier versions of the ETL jobs. The framework is portable, easy to deploy across different sites and even further adaptable to other database schemes. CONCLUSION: The presented framework provides the first step towards a unified, standardized and harmonized EHR DQ assessment in MIRACUM. DQ issues can now be systematically identified by individual hospitals to subsequently implement site- or consortium-wide feedback loops to increase data quality.
CRIS-Team Scopus Importer:2019-09-24
Keywords: clinical research; Data analysis; data quality; electronic health record

Multinational characterization of neurological phenotypes in patients hospitalized with COVID-19 - Le, Trang T.; Gutierrez-Sacristan, Alba; Son, Jiyeon; Hong, Chuan; South, Andrew M.; Beaulieu-Jones, Brett K.; Loh, Ne Hooi Will; Luo, Yuan; Morris, Michele; Ngiam, Kee Yuan; Patel, Lav P.; Samayamuthu, Malarkodi J.; et al. (Scientific Reports, 2021)
Neurological complications worsen outcomes in COVID-19. To define the prevalence of neurological conditions among hospitalized patients with a positive SARS-CoV-2 reverse transcription polymerase chain reaction test in geographically diverse multinational populations during early pandemic, we used electronic health records (EHR) from 338 participating hospitals across 6 countries and 3 continents (January–September 2020) for a cross-sectional analysis. We assessed the frequency of International Classification of Disease code of neurological conditions by countries, healthcare systems, time before and after admission for COVID-19 and COVID-19 severity. Among 35,177 hospitalized patients with SARS-CoV-2 infection, there was an increase in the proportion with disorders of consciousness (5.8%, 95% confidence interval [CI] 3.7–7.8%, pFDR < 0.001) and unspecified disorders of the brain (8.1%, 5.7–10.5%, pFDR < 0.001) when compared to the pre-admission proportion. During hospitalization, the relative risk of disorders of consciousness (22%, 19–25%), cerebrovascular diseases (24%, 13–35%), nontraumatic intracranial hemorrhage (34%, 20–50%), encephalitis and/or myelitis (37%, 17–60%) and myopathy (72%, 67–77%) were higher for patients with severe COVID-19 when compared to those who never experienced severe COVID-19. Leveraging a multinational network to capture standardized EHR data, we highlighted the increased prevalence of central and peripheral neurological phenotypes in patients hospitalized with COVID-19, particularly among those with severe disease.
CRIS-Team Scopus Importer:2021-11-05

Multiple breath washout (MBW) testing using sulfur hexafluoride: Reference values and influence of anthropometric parameters - Trinkmann, Frederik; Maros, Mate; Roth, Katharina; Hermanns, Arne; Schaefer, Julia; Gawlitza, Joshua; Saur, Joachim; Akin, Ibrahim; Borggrefe, Martin; Herth, Felix J. F.; Ganslandt, Thomas (Thorax, 2021)
Background Multiple breath washout (MBW) using sulfur hexafluoride (SF 6) has the potential to reveal ventilation heterogeneity which is frequent in patients with obstructive lung disease and associated small airway dysfunction. However, reference data are scarce for this technique and mostly restricted to younger cohorts. We therefore set out to evaluate the influence of anthropometric parameters on SF 6-MBW reference values in pulmonary healthy adults. Methods We evaluated cross-sectional data from 100 pulmonary healthy never-smokers and smokers (mean 51 (SD 20), range 20-88 years). Lung clearance index (LCI), acinar (S acin) and conductive (S cond) ventilation heterogeneity were derived from triplicate SF 6-MBW measurements. Global ventilation heterogeneity was calculated for the 2.5% (LCI 2.5) and 5% (LCI 5) stopping points. Upper limit of normal (ULN) was defined as the 95th percentile. Results Age was the only meaningful parameter influencing SF 6-MBW parameters, explaining 47% (CI 33% to 59%) of the variance in LCI, 32% (CI 18% to 47%) in S acin and 10% (CI 2% to 22%) in S cond. Mean LCI increases from 6.3 (ULN 7.4) to 8.8 (ULN 9.9) in subjects between 20 and 90 years. Smoking accounted for 2% (CI 0% to 8%) of the variability in LCI, 4% (CI 0% to 13%) in S acin and 3% (CI 0% to 13%) in S cond. Conclusion SF 6-MBW outcome parameters showed an age-dependent increase from early adulthood to old age. The effect was most pronounced for global and acinar ventilation heterogeneity and smaller for conductive ventilation heterogeneity. No influence of height, weight and sex was seen. Reference values can now be provided for all important SF 6-MBW outcome parameters over the whole age range. Trial registration number NCT04099225.
CRIS-Team Scopus Importer:2021-11-16
Keywords: respiratory measurement

No mathematical shortcuts for standardization or harmonization of laboratory measurements - Winter, Christof; Ganslandt, Thomas; Bietenbeck, Andreas (Laboratoriumsmedizin-Journal of Laboratory Medicine, 2018)
EVALuna2:34035

Numerical parameters and quality indicators in a Medical Emergency Department Kennzahlen und qualitätsindikatoren einer medizinischen notaufnahme - Dormann, Harald; Diesch, Katharina; Ganslandt, Thomas; Hahn, Eckhart Georg (Deutsches Ärzteblatt, 2010)
Background: Despite calls for improved quality and efficiency in medical emergency departments, there exist hardly any quality indicators, and no methods of calculating efficiency have been published to date. The present study illustrates a means of presenting numerical parameters of a medical emergency department and of identifying potential quality indicators. Methods: Over a period of 12 months, all patient contacts of the medical emergency department in the University hospital of Erlangen were analyzed with respect to patient flow, diagnoses, and treatment units. The diagnostic agreement (DA) parameter was calculated from a systematic comparison of admitting and discharge diagnoses, and diagnostic efficiency (DE) was defined and calculated as the quotient of DA x100 divided by the length of stay in the emergency department. Results: Among the 6683 patients treated, 64.6% underwent further in-hospital care. The diagnostic spectrum of the outpatients differed markedly from that of the inpatients. Patients with diseases of the heart, gastrointestinal tract, and lungs were usually admitted to the hospital for further treatment. Patient contacts had a characteristic circadian and weekly rhythmic pattern. For the overall patient collective, the DA was 71%. The mean length of stay in the emergency department was 116 minutes, and the DE was therefore 0.61/min. The DA was highest (92%) among patients with atrial flutter or fibrillation, while the DE was highest (0.85/min) among patients with acute myocardial infarction. 14% of the patients required further treatment in intensive care. Conclusion: Numerical parameters and quality indicators for a hospital emergency department can be presented in transparent fashion. DA and DE can be used as parameters for diagnosis-related and intradepartmental quality assessment. © 2010 Deutsches Ärzteblatt, Deutscher Ärzte-Verlag GmbH.
CRIS-Team Scopus Importer:2021-11-16

Perspectives for medical informatics - reusing the electronic medical record for clinical research - Prokosch, Hans-Ulrich; Ganslandt, Thomas (Methods of Information in Medicine, 2009)
Objectives: Even though today most university hospitals have already implemented commercial hospital information systems and started to build up comprehensive electronic medical records, reuse of such data for data warehousing and research purposes is still very rare. Given this situation, the focus of this paper is to present an overview on exemplary projects, which have already tackled this challenge, reflect on current initiatives within the United States of America and the European Union to establish IT infrastructures for clinical and translational research, and draw attention to new challenges in this area. Methods: This paper does not intend to provide a fully comprehensive review on all the issues of clinical routine data reuse. It is based, however, on a presentation of a large variety of historical, but also most recent activities in data warehousing, data retrieval and linking medical informatics with translational research. Results: The article presents an overview of the various international approaches to this issue and illustrates concepts and solutions which have been published, thus giving an impression of activities pursued in this field of medical informatics. Further, problems and open questions, which have also been named in the literature, are presented and three challenges (to establish comprehensive clinical data warehouses, to establish professional IT infrastructure applications supporting clinical trial data capture and to integrate medical record systems and clinical trial databases) related to this area of medical informatics are identified and presented. Conclusions: Translational biomedical research with the aim "to integrate bedside and biology" and to bridge the gap between clinical care and medical research today and in the years to come, provides a large and interesting field for medical informatics researchers. Especially the need for integrating clinical research projects with data repositories built up during documentation of routine clinical care, today still leaves many open questions and research challenges. Consideration of regulatory requirements, data privacy issues, data standards as well as people/organizational issues are prerequisites in order to vanquish existing obstacles. © Schattauer 2009.
UnivIS-Import:2015-03-09:Pub.2009.tech.IMMD.infome.perspe
Keywords: Data warehousing; IT infrastructure for clinical research; Reuse of EMR data; Translational biomedical research

Prospective trial on telemonitoring of geriatric cancer patients using handheld devices - Buergy, Daniel; Siefert, Victor; Neumaier, Christian; Ganslandt, Thomas; Sperk, Elena; Blessing, Manuel; Hesser, Juergen; Welzel, Grit; Wenz, Frederik; Giordano, Frank Anton (Strahlentherapie und Onkologie, 2020)
Purpose and objective: Randomized trials indicate that electronic or app-based assessment of patient-reported outcomes may improve outcomes in cancer patients. To analyze if an app-based follow-up would be accepted by elderly cancer patients, we conducted a single-center prospective feasibility study (NCT03196050). Materials and methods: Cancer patients (≥60 years) without concurrent uncontrolled severe medical conditions and a Karnofsky performance status (KPS) ≥70 were eligible if they were able to use the smartphone app. The primary endpoint was compliance over 1 year, calculated as patient-specific and study date-specific response rate to questions sent as push notifications; in this interim analysis, we report on 4‑month data. Secondary outcomes included a comparison of a subjective health status item (SPHS) with the physician-rated KPS. Results: Out of 225 patients screened, 54 patients agreed to participate and 29 activated the app and participated in the study. The mean age was 66 years (61–78). The individual compliance rate averaged at 58.3% (standard deviation SD = 35%). Daily compliance was 53.3% on average (SD = 10.8%) and declined over time. The average percentage of patients who sent answers at least weekly was 75.0% (SD = 14.8%) and declined from 100% in week 1 to 53.8% in week 17 post-enrollment. Secondary outcomes indicated that questionnaires such as the EORTC-QLQ-C30 are accepted via app and that there is a significant moderate correlation between the SPHS and KPS scores (r = 0.566; p < 0.001). Conclusion: Our data indicate that an app-based follow-up incorporating EORTC questionnaires might be possible in highly selected elderly cancer patients with modest compliance rates. Further trials should aim at an increased participation rate.
CRIS-Team Scopus Importer:2021-11-16
Keywords: App-based follow-up; App-based reporting; Electronic health assessment; Mobile health; Patient-reported outcomes

Provenance Information for Biomedical Data and Workflows: Scoping Review - Gierend, Kerstin; Krüger, Frank; Genehr, Sascha; Hartmann, Francisca; Siegel, Fabian; Waltemath, Dagmar; Ganslandt, Thomas; Zeleke, Atinkut Alamirrew (Journal of Medical Internet Research, 2024)
Background: The record of the origin and the history of data, known as provenance, holds importance. Provenance information leads to higher interpretability of scientific results and enables reliable collaboration and data sharing. However, the lack of comprehensive evidence on provenance approaches hinders the uptake of good scientific practice in clinical research. Objective: This scoping review aims to identify approaches and criteria for provenance tracking in the biomedical domain. We reviewed the state-of-the-art frameworks, associated artifacts, and methodologies for provenance tracking. Methods: This scoping review followed the methodological framework developed by Arksey and O’Malley. We searched the PubMed and Web of Science databases for English-language articles published from 2006 to 2022. Title and abstract screening were carried out by 4 independent reviewers using the Rayyan screening tool. A majority vote was required for consent on the eligibility of papers based on the defined inclusion and exclusion criteria. Full-text reading and screening were performed independently by 2 reviewers, and information was extracted into a pretested template for the 5 research questions. Disagreements were resolved by a domain expert. The study protocol has previously been published. Results: The search resulted in a total of 764 papers. Of 624 identified, deduplicated papers, 66 (10.6%) studies fulfilled the inclusion criteria. We identified diverse provenance-tracking approaches ranging from practical provenance processing and managing to theoretical frameworks distinguishing diverse concepts and details of data and metadata models, provenance components, and notations. A substantial majority investigated underlying requirements to varying extents and validation intensities but lacked completeness in provenance coverage. Mostly, cited requirements concerned the knowledge about data integrity and reproducibility. Moreover, these revolved around robust data quality assessments, consistent policies for sensitive data protection, improved user interfaces, and automated ontology development. We found that different stakeholder groups benefit from the availability of provenance information. Thereby, we recognized that the term provenance is subjected to an evolutionary and technical process with multifaceted meanings and roles. Challenges included organizational and technical issues linked to data annotation, provenance modeling, and performance, amplified by subsequent matters such as enhanced provenance information and quality principles. Conclusions: As data volumes grow and computing power increases, the challenge of scaling provenance systems to handle data efficiently and assist complex queries intensifies, necessitating automated and scalable solutions. With rising legal and scientific demands, there is an urgent need for greater transparency in implementing provenance systems in research projects, despite the challenges of unresolved granularity and knowledge bottlenecks. We believe that our recommendations enable quality and guide the implementation of auditable and measurable provenance approaches as well as solutions in the daily tasks of biomedical scientists.
CRIS-Team Scopus Importer:2024-09-06
Keywords: biomedical research; data management; health care data; provenance; scoping review; software life cycle

Rapid Convolutional Neural Networks for Gram-Stained Image Classification at Inference Time on Mobile Devices: Empirical Study from Transfer Learning to Optimization - Kim, Hee E.; Maros, Mate E.; Siegel, Fabian; Ganslandt, Thomas (Biomedicines, 2022)
Despite the emergence of mobile health and the success of deep learning (DL), deploying production-ready DL models to resource-limited devices remains challenging. Especially, during inference time, the speed of DL models becomes relevant. We aimed to accelerate inference time for Gram-stained analysis, which is a tedious and manual task involving microorganism detection on whole slide images. Three DL models were optimized in three steps: transfer learning, pruning and quantization and then evaluated on two Android smartphones. Most convolutional layers (≥80%) had to be retrained for adaptation to the Gram-stained classification task. The combination of pruning and quantization demonstrated its utility to reduce the model size and inference time without compromising model quality. Pruning mainly contributed to model size reduction by 15×, while quantization reduced inference time by 3× and decreased model size by 4×. The combination of two reduced the baseline model by an overall factor of 46×. Optimized models were smaller than 6 MB and were able to process one image in <0.6 s on a Galaxy S10. Our findings demonstrate that methods for model compression are highly relevant for the successful deployment of DL solutions to resource-limited devices.
CRIS-Team Scopus Importer:2022-11-25
Keywords: deep learning; Gram-stained classification; mHealth; model compression; pruning; quantization; rapid inference time

Regional Differences in Thrombectomy Rates : Secondary use of Billing Codes in the MIRACUM (Medical Informatics for Research and Care in University Medicine) Consortium - Haverkamp, Christian; Ganslandt, Thomas; Horki, Petar; Boeker, Martin; Dörfler, Arnd; Schwab, Stefan; Berkefeld, Joachim; Pfeilschifter, Waltraud; Niesen, Wolf-Dirk; Egger, Karl; Kaps, Manfred; Brockmann, Marc A.; et al. (Clinical Neuroradiology, 2018)
Mechanical thrombectomy, in addition to intravenous (i.v.) thrombolysis is recommended for treatment of acute stroke in patients with large vessel occlusions (LVO) in the anterior circulation up to 6 h after symptom onset. We compared thrombectomy rates of eight university hospitals of the MIRACUM consortium to analyze the implementation of this guideline in clinical routine.Anonymized billing data in a standardized format were loaded into a local i2b2 data warehouse by applying already existing extract, transform and load (ETL) routines. A locally executed uniform SQL (structured query language) query delivered aggregated site data for all inpatients with a discharge diagnosis of ischemic stroke (ICD-10 I63) containing counts for type of acute treatment, type of admission and age groups, which were centrally analyzed with R.From 2014 to 2016, the thrombectomy rate almost doubled from a mean of 4.7% to 9.6%, although significant differences between centers exist (range in 2016: 5.8-17%). The number of drip-and-ship procedures increased in 3 out of 8 centers. There was no evidence for a decrease in thrombectomy rates during weekends/holiday or among patients older than 80 years, but this age group is more likely to receive i.v. recombinant tissue plasminogen activator (rtPA).The observed increase of thrombectomy rates and drip-and-ship procedures without a significant difference between weekdays and weekends or patients of different ages is substantiating a rapid implementation of stroke guidelines within the analyzed neurovascular centers. The prototype of the MIRACUM Data Integration Center already contributes to health services research in Germany.
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Registries in Clinical Epidemiology: the European Surveillance System on Contact Allergies (ESSCA) - Uter, Wolfgang; Schnuch, A.; Wilkinson, M.; Dugonik, A.; Dugonik, B.; Ganslandt, Thomas (Methods of Information in Medicine, 2016)
Disease registries rely on consistent electronic data capturing (EDC) pertinent to their objectives; either by using existing electronic data as far as available, or by implementing specific software solutions.To describe the current practice of an international disease registry (European Surveillance System on Contact Allergies, ESSCA, www.essca-dc.org) against different state of the art approaches for EDC.Since 2002, ESSCA is collecting data, currently from 53 departments in 12 countries. Departmental EDC software ranges from spreadsheets to comprehensive "patch test software" based on a relational database. In the Erlangen data centre, such diverse data is imported, converted to a common format, quality checked and pooled for scientific analyses.Feed-back to participating departments for quality control is provided by standardised reports. Varying author teams publish scientific analyses addressing the objective of contact allergy surveillance.Although ESSCA represents a historically grown, heterogeneous network and not one unified approach to EDC, some of its features have contributed to its viability in the last 12 years and may be useful to consider for similar investigator-initiated networks.
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Shaping the digital transformation of laboratory medicine - Bietenbeck, Andreas; Ganslandt, Thomas (Journal of Laboratory Medicine, 2018)
CRIS-Team Scopus Importer:2021-11-16

Simplified deployment of health informatics applications by providing docker images - Loebe, Matthias; Ganslandt, Thomas; Lotzmann, Lydia; Mate, Sebastian; Christoph, Jan; Baum, Benjamin; Sariyar, Murat; Wu, Jie; Staeubert, Sebastian (Studies in Health Technology and Informatics, 2017)
Due to the specific needs of biomedical researchers, in-house development of software is widespread. A common problem is to maintain and enhance software after the funded project has ended. Even if many tools are made open source, only a couple of projects manage to attract a user basis large enough to ensure sustainability. Reasons for this include complex installation and configuration of biomedical software as well as an ambiguous terminology of the features provided; all of which make evaluation of software laborious. Docker is a para-virtualization technology based on Linux containers that eases deployment of applications and facilitates evaluation. We investigated a suite of software developments funded by a large umbrella organization for networked medical research within the last 10 years and created Docker containers for a number of applications to support utilization and dissemination.
CRIS-Team Scopus Importer:2021-11-16
Keywords: Cloud; Container; Deployment; DevOps; Docker; IT infrastructure; Microservices; PaaS

Simplified Generation of Biomedical 3D Surface Model Data for Embedding into 3D Portable Document Format (PDF) Files for Publication and Education - Newe, Axel; Ganslandt, Thomas (PLoS ONE, 2013)
The usefulness of the 3D Portable Document Format (PDF) for clinical, educational, and research purposes has recently been shown. However, the lack of a simple tool for converting biomedical data into the model data in the necessary Universal 3D (U3D) file format is a drawback for the broad acceptance of this new technology. A new module for the image processing and rapid prototyping framework MeVisLab does not only provide a platform-independent possibility to create surface meshes out of biomedical/DICOM and other data and to export them into U3D – it also lets the user add meta data to these meshes to predefine colors and names that can be processed by a PDF authoring software while generating 3D PDF files. Furthermore, the source code of the respective module is available and well documented so that it can easily be modified for own purposes.
UnivIS-Import:2015-03-09:Pub.2013.tech.IMMD.infome.simpli

Standards-Based Procedural Phenotyping: The Arden Syntax on i2b2 - Mate, Sebastian; Castellanos, Ixchel; Ganslandt, Thomas; Prokosch, Hans-Ulrich; Kraus, Stefan (Studies in health technology and informatics, 2017)
Phenotyping, or the identification of patient cohorts, is a recurring challenge in medical informatics. While there are open source tools such as i2b2 that address this problem by providing user-friendly querying interfaces, these platforms lack semantic expressiveness to model complex phenotyping algorithms. The Arden Syntax provides procedural programming language construct, designed specifically for medical decision support and knowledge transfer. In this work, we investigate how language constructs of the Arden Syntax can be used for generic phenotyping. We implemented a prototypical tool to integrate i2b2 with an open source Arden execution environment. To demonstrate the applicability of our approach, we used the tool together with an Arden-based phenotyping algorithm to derive statistics about ICU-acquired hypernatremia. Finally, we discuss how the combination of i2b2's user-friendly cohort pre-selection and Arden's procedural expressiveness could benefit phenotyping.
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The clinical data intelligence project - Sonntag, Daniel; Tresp, Volker; Zillner, Sonja; Hammon, Matthias; Reis, André; Fasching, Peter; Sedlmayr, Martin; Ganslandt, Thomas; Prokosch, Hans-Ulrich; Budde, Clemens; Schmidt, Danilo; Hinrichs, Carl; et al. (Informatik-Spektrum, 2015)
UnivIS-Import:2017-12-18:Pub.2015.tech.IMMD.infome.thecli

The experience sampling methodology as a digital clinical tool for more person-centered mental health care: an implementation research agenda - Myin-Germeys, Inez; Schick, Anita; Ganslandt, Thomas; Hajdúk, Michal; Heretik, Anton; Van Hoyweghen, Ine; Kiekens, Glenn; Koppe, Georgia; Marelli, Luca; Nagyova, Iveta; Weermeijer, Jeroen; Wensing, Michel; et al. (Psychological Medicine, 2024)
This position paper by the international IMMERSE consortium reviews the evidence of a digital mental health solution based on Experience Sampling Methodology (ESM) for advancing person-centered mental health care and outlines a research agenda for implementing innovative digital mental health tools into routine clinical practice. ESM is a structured diary technique recording real-time self-report data about the current mental state using a mobile application. We will review how ESM may contribute to (1) service user engagement and empowerment, (2) self-management and recovery, (3) goal direction in clinical assessment and management of care, and (4) shared decision-making. However, despite the evidence demonstrating the value of ESM-based approaches in enhancing person-centered mental health care, it is hardly integrated into clinical practice. Therefore, we propose a global research agenda for implementing ESM in routine mental health care addressing six key challenges: (1) the motivation and ability of service users to adhere to the ESM monitoring, reporting and feedback, (2) the motivation and competence of clinicians in routine healthcare delivery settings to integrate ESM in the workflow, (3) the technical requirements and (4) governance requirements for integrating these data in the clinical workflow, (5) the financial and competence related resources related to IT-infrastructure and clinician time, and (6) implementation studies that build the evidence-base. While focused on ESM, the research agenda holds broader implications for implementing digital innovations in mental health. This paper calls for a shift in focus from developing new digital interventions to overcoming implementation barriers, essential for achieving a true transformation toward person-centered care in mental health.
CRIS-Team Scopus Importer:2024-10-04
Keywords: digital mental health; experience sampling methodology; implementation; person-centered care; self-management

The Status of Data Management Practices Across German Medical Data Integration Centers: Mixed Methods Study - Gierend, Kerstin; Freiesleben, Sherry; Kadioglu, Dennis; Siegel, Fabian; Ganslandt, Thomas; Waltemath, Dagmar (Journal of Medical Internet Research, 2023)
BACKGROUND: In the context of the Medical Informatics Initiative, medical data integration centers (DICs) have implemented complex data flows to transfer routine health care data into research data repositories for secondary use. Data management practices are of importance throughout these processes, and special attention should be given to provenance aspects. Insufficient knowledge can lead to validity risks and reduce the confidence and quality of the processed data. The need to implement maintainable data management practices is undisputed, but there is a great lack of clarity on the status. OBJECTIVE: Our study examines the current data management practices throughout the data life cycle within the Medical Informatics in Research and Care in University Medicine (MIRACUM) consortium. We present a framework for the maturity status of data management practices and present recommendations to enable a trustful dissemination and reuse of routine health care data. METHODS: In this mixed methods study, we conducted semistructured interviews with stakeholders from 10 DICs between July and September 2021. We used a self-designed questionnaire that we tailored to the MIRACUM DICs, to collect qualitative and quantitative data. Our study method is compliant with the Good Reporting of a Mixed Methods Study (GRAMMS) checklist. RESULTS: Our study provides insights into the data management practices at the MIRACUM DICs. We identify several traceability issues that can be partially explained with a lack of contextual information within nonharmonized workflow steps, unclear responsibilities, missing or incomplete data elements, and incomplete information about the computational environment information. Based on the identified shortcomings, we suggest a data management maturity framework to reach more clarity and to help define enhanced data management strategies. CONCLUSIONS: The data management maturity framework supports the production and dissemination of accurate and provenance-enriched data for secondary use. Our work serves as a catalyst for the derivation of an overarching data management strategy, abiding data integrity and provenance characteristics as key factors. We envision that this work will lead to the generation of fairer and maintained health research data of high quality.
CRIS-Team Scopus Importer:2023-11-24
Keywords: data integration center; data management; maturity model; metadata; provenance; traceability

The Status Quo of Rare Diseases Centres for the Development of a Clinical Decision Support System - A Cross-Sectional Study - Schaaf, Jannik; Sedlmayr, Martin; Prokosch, Hans-Ulrich; Ganslandt, Thomas; Schade-Brittinger, Carmen; von Wagner, Michael; Kadioglu, Dennis; Schubert, Katharina; Lee-Kirsch, Min Ae; Kraemer, Bernhard K.; Winner, Beate; Mueller, Tobias; et al. (dHealth 2020 – Biomedical Informatics for Health and Care, 2020)
Clinical decision support systems (CDSS) help to improve the diagnostics and treatment of rare diseases (RD). As one of four funded consortia of the Medical Informatics Initiative supported by the Federal Ministry of Education and Research (BMBF, Germany), MIRACUM develops a clinical decision support system (CDSS) for RD based on distributed data of ten university hospitals. The CDSS will be developed at the Rare Diseases Centres (RDC) of the MIRACUM consortium. Since it is essential to deliver decision support at the right time and place in the clinician's workflow, this study aimed to capture relevant information of the RDCs regarding patient admission and diagnostic process. Additionally, we investigated how patient documentation and digitalisation is performed at the centres. Therefore, we conducted a cross-sectional survey involving experts in the RDs domain to capture relevant information for the further development of a CDSS in RD. For each centre, one expert on RDs participated in the study (n=8). The survey identified several challenges regarding the reuse of patient data, e.g. the paper-based documentation of a patientâĂŹs medical history and coding of diagnoses using ICD-10. However, we noticed a relevant use of current software diagnosis support and a similarly performed diagnostic process in all RDC. Further studies are needed to get more detailed insights and to define specific requirements.
CRIS-Team Scopus Importer:2020-07-10
Keywords: clinical decision support; quantitative analysis; rare diseases

ToolPool Gesundheitsforschung - A Repository for Software and Services Focused on Supporting Clinical and Epidemiological Research. - Löbe, Matthias; Bialke, Martin; Bienzeisler, Jonas; Drepper, Johannes; Ganslandt, Thomas; Haderer, Sophie; Kraska, Detlef; Lablans, Martin; Sax, Ulrich; Speer, Ronald; Stäubert, Sebastian; Kaulke, Knut (Studies in health technology and informatics, 2022)
The academic research environment is characterized by self-developed, innovative, customized solutions, which are often free to use for third parties with open-source code and open licenses. On the other hand, they are maintained only to a very limited extent after the end of project funding. The ToolPool Gesundheitsforschung addresses the problem of finding ready to use solutions by building a registry of proven and supported tools, services, concepts and consulting offers. The goal is to provide an up-to-date selection of "relevant" solutions for a given domain that are immediately usable and that are actually used by third parties, rather than aiming at a complete list of all solutions which belong to that domain. Proof of relevance and usage must be provided, for example, by concrete application scenarios, experience reports by uninvolved third parties, references in publications or workshops held. Quality assurance is carried out for new entries by an agreed list of admission criteria, for existing entries at least once a year by a special task force. Currently, 79 solutions are represented, this number is to be significantly expanded by involving of new editors from current national funding initiatives in Germany.
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Towards a comprehensive and interoperable representation of consent-based data usage permissions in the German medical informatics initiative - Bild, Raffael; Bialke, Martin; Buckow, Karoline; Ganslandt, Thomas; Ihrig, Kristina; Jahns, Roland; Merzweiler, Angela; Roschka, Sybille; Schreiweis, Bjoern; Staeubert, Sebastian; Zenker, Sven; Prasser, Fabian (BMC Medical Informatics and Decision Making, 2020)
Background: The aim of the German Medical Informatics Initiative is to establish a national infrastructure for integrating and sharing health data. To this, Data Integration Centers are set up at university medical centers, which address data harmonization, information security and data protection. To capture patient consent, a common informed consent template has been developed. It consists of different modules addressing permissions for using data and biosamples. On the technical level, a common digital representation of information from signed consent templates is needed. As the partners in the initiative are free to adopt different solutions for managing consent information (e.g. IHE BPPC or HL7 FHIR Consent Resources), we had to develop an interoperability layer. Methods: First, we compiled an overview of data items required to reflect the information from the MII consent template as well as patient preferences and derived permissions. Next, we created entity-relationship diagrams to formally describe the conceptual data model underlying relevant items. We then compared this data model to conceptual models describing representations of consent information using different interoperability standards. We used the result of this comparison to derive an interoperable representation that can be mapped to common standards. Results: The digital representation needs to capture the following information: (1) version of the consent, (2) consent status for each module, and (3) period of validity of the status. We found that there is no generally accepted solution to represent status information in a manner interoperable with all relevant standards. Hence, we developed a pragmatic solution, comprising codes which describe combinations of modules with a basic set of status labels. We propose to maintain these codes in a public registry called ART-DECOR. We present concrete technical implementations of our approach using HL7 FHIR and IHE BPPC which are also compatible with the open-source consent management software gICS. Conclusions: The proposed digital representation is (1) generic enough to capture relevant information from a wide range of consent documents and data use regulations and (2) interoperable with common technical standards. We plan to extend our model to include more fine-grained status codes and rules for automated access control.
CRIS-Team Scopus Importer:2021-11-16
Keywords: Consent template; Data integration centers; Health information interoperability; Informed consent; Medical informatics initiative

Towards a Computable Data Corpus of Temporal Correlations between Drug Administration and Lab Value Changes - Newe, Axel; Wimmer, Stefan; Neubert, Antje; Becker, Linda; Prokosch, Hans-Ulrich; Beckmann, Matthias; Fietkau, Rainer; Forster, Christian; Neurath, Markus; Schett, Georg; Ganslandt, Thomas (PLoS ONE, 2015)
Background The analysis of electronic health records for an automated detection of adverse drug reactions is an approach to solve the problems that arise from traditional methods like spontaneous reporting or manual chart review. Algorithms addressing this task should be modeled on the criteria for a standardized case causality assessment defined by the World Health Organization. One of these criteria is the temporal relationship between drug intake and the occurrence of a reaction or a laboratory test abnormality. Appropriate data that would allow for developing or validating related algorithms is not publicly available, though. Methods In order to provide such data, retrospective routine data of drug administrations and temporally corresponding laboratory observations from a university clinic were extracted, transformed and evaluated by experts in terms of a reasonable time relationship between drug administration and lab value alteration. Result The result is a data corpus of 400 episodes of normalized laboratory parameter values in temporal context with drug administrations. Each episode has been manually classified whether it contains data that might indicate a temporal correlation between the drug administration and the change of the lab value course, whether such a change is not observable or whether a decision between those two options is not possible due to the data. In addition, each episode has been assigned a concordance value which indicates how difficult it is to assess. This is the first open data corpus of a computable ground truth of temporal correlations between drug administration and lab value alterations. Discussion The main purpose of this data corpus is the provision of data for further research and the provision of a ground truth which allows for comparing the outcome of other assessments of this data with the outcome of assessments made by human experts. It can serve as a contribution towards systematic, computerized ADR detection in retrospective data. With this lab value curve data as a basis, algorithms for detecting temporal relationships can be developed, and with the classification made by human experts, these algorithms can immediately be validated. Due to the normalization of the lab value data, it allows for a generic approach rather than for specific or solitary drug/lab value combination

Towards a medication core data set for the medical Informatics Initiative (MII): Initial mapping experience between the German procedure classification (OPS) and the identification of medicinal products (IDMP) - Zabka, Susanne; Ammon, Danny; Ganslandt, Thomas; Gewehr, Jan; Haverkamp, Christian; Kiefer, Saskia; Lautenbacher, Heinrich; Löbe, Matthias; Thun, Sylvia; Boeker, Martin (CEUR Workshop Proceedings, 2019)
Documentation of medication processes is among the most difficult tasks associated with digital clinical documentation. Despite its great potential medical impact, it has not been consistently adopted in German university hospitals, with one of the reasons being the lack of a comprehensive information model with sound terminological support. With the Identification of Medicinal Products (IDMP), several international authorities have recently provided a comprehensive set of standards for the formal description of drugs, mainly for regulatory purposes. In this paper, we describe the approach of the German Medical Informatics Initiative (MII) towards the modelling of a medication core dataset using IDMP. Three different process models with minimal datasets for medication were defined: summative documentation of episodes, point-wise documentation, and medication plans (snapshot documentation). As a feasibility study evaluating how standard terminologies can be used for a minimal data set of hospital medication data, a mapping between IDMP terminologies and the medication chapter of the German Procedure Classification “OPS” (Operationenund Prozedurenschlüssel) was developed. About 1700 OPS classes, representing about 160 highly reimbursement relevant drugs, were mapped onto different axes of IDMP (substances, optionally: routes of administration and units) using several coding systems with different scopes, to analyze their coverage and particular advantages. A mapping was possible for almost all OPS codes. For the substances, it is necessary to distinguish between the common substance name and the active ingredient, as these may be different for a drug. This “lightweight” mapping approach demonstrates the basic feasibility of using IDMP for medication documentation.
CRIS-Team Scopus Importer:2021-11-16
Keywords: Data standards; German Medical Informatics Initiative; Identification of Medicinal Products (IDMP); Medication data; Standards mapping; Terminology

Towards a smart object network for clinical services. - Sedlmayr, Martin; Becker, Andreas; Münch, Ulli; Meier, Fritz; Prokosch, Hans-Ulrich; Ganslandt, Thomas (Annual Symposium proceedings, 2009)
Radio Frequency Identification (RFID) technology has become prevalent in logistics to support and optimize processes and numerous projects investigate its opportunities and challenges in the clinical context. However, most approaches are focused on one specific application using only a few possibilities of the technology. We describe the development of a smart object network to serve as a generic platform for various clinical services. Based on two disparate scenarios - tracking medical devices and transfusion safety - the architecture, implementation issues and prospects of such a platform are discussed.
CRIS-Team Scopus Importer:2021-11-16

Towards Implementation of OMOP in a German University Hospital Consortium - Maier, Carolin; Lang, L.; Storf, H.; Vormstein, P.; Bieber, R.; Bernarding, J.; Herrmann, T.; Haverkamp, C.; Horki, P.; Laufer, J.; Berger, F.; Hoening, G.; et al. (Applied Clinical Informatics, 2018)
In 2015, the German Federal Ministry of Education and Research initiated a large data integration and data sharing research initiative to improve the reuse of data from patient care and translational research. The Observational Medical Outcomes Partnership (OMOP) common data model and the Observational Health Data Sciences and Informatics (OHDSI) tools could be used as a core element in this initiative for harmonizing the terminologies used as well as facilitating the federation of research analyses across institutions. To realize an OMOP/OHDSI-based pilot implementation within a consortium of eight German university hospitals, evaluate the applicability to support data harmonization and sharing among them, and identify potential enhancement requirements. The vocabularies and terminological mapping required for importing the fact data were prepared, and the process for importing the data from the source files was designed. For eight German university hospitals, a virtual machine preconfigured with the OMOP database and the OHDSI tools as well as the jobs to import the data and conduct the analysis was provided. Last, a federated/distributed query to test the approach was executed. While the mapping of ICD-10 German Modification succeeded with a rate of 98.8% of all terms for diagnoses, the procedures could not be mapped and hence an extension to the OMOP standard terminologies had to be made.Overall, the data of 3 million inpatients with approximately 26 million conditions, 21 million procedures, and 23 million observations have been imported.A federated query to identify a cohort of colorectal cancer patients was successfully executed and yielded 16,701 patient cases visualized in a Sunburst plot. OMOP/OHDSI is a viable open source solution for data integration in a German research consortium. Once the terminology problems can be solved, researchers can build on an active community for further development.
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Towards structured data quality assessment in the German medical informatics initiative: Initial approach in the MII demonstrator study - Kamdje-Wabo, Gaetan; Gradinger, Tobias; Löbe, Matthias; Lodahl, Robert; Seuchter, Susanne Andrea; Sax, Ulrich; Ganslandt, Thomas (Studies in Health Technology and Informatics, 2019)
The Demonstrator study aims to analyse comorbidities and rare diseases among patients from German university hospitals within the German Medical Informatics Initiative. This work aimed to design and determine the feasibility of a model to assess the quality of the claims data used in the study. Several data quality issues were identified affecting small amounts of cases in one of the participating sites. As a next step an extension to all participating sites is planned.
CRIS-Team Scopus Importer:2019-09-17
Keywords: Data Accuracy; Electronic Health Records; Medical Informatics

Traceable Research Data Sharing in a German Medical Data Integration Center With FAIR (Findability, Accessibility, Interoperability, and Reusability)-Geared Provenance Implementation: Proof-of-Concept Study - Gierend, Kerstin; Waltemath, Dagmar; Ganslandt, Thomas; Siegel, Fabian (JMIR Formative Research, 2023)
Background: Secondary investigations into digital health records, including electronic patient data from German medical data integration centers (DICs), pave the way for enhanced future patient care. However, only limited information is captured regarding the integrity, traceability, and quality of the (sensitive) data elements. This lack of detail diminishes trust in the validity of the collected data. From a technical standpoint, adhering to the widely accepted FAIR (Findability, Accessibility, Interoperability, and Reusability) principles for data stewardship necessitates enriching data with provenance-related metadata. Provenance offers insights into the readiness for the reuse of a data element and serves as a supplier of data governance. Objective: The primary goal of this study is to augment the reusability of clinical routine data within a medical DIC for secondary utilization in clinical research. Our aim is to establish provenance traces that underpin the status of data integrity, reliability, and consequently, trust in electronic health records, thereby enhancing the accountability of the medical DIC. We present the implementation of a proof-of-concept provenance library integrating international standards as an initial step. Methods: We adhered to a customized road map for a provenance framework, and examined the data integration steps across the ETL (extract, transform, and load) phases. Following a maturity model, we derived requirements for a provenance library. Using this research approach, we formulated a provenance model with associated metadata and implemented a proof-of-concept provenance class. Furthermore, we seamlessly incorporated the internationally recognized Word Wide Web Consortium (W3C) provenance standard, aligned the resultant provenance records with the interoperable health care standard Fast Healthcare Interoperability Resources, and presented them in various representation formats. Ultimately, we conducted a thorough assessment of provenance trace measurements. Results: This study marks the inaugural implementation of integrated provenance traces at the data element level within a German medical DIC. We devised and executed a practical method that synergizes the robustness of quality- and health standard–guided (meta)data management practices. Our measurements indicate commendable pipeline execution times, attaining notable levels of accuracy and reliability in processing clinical routine data, thereby ensuring accountability in the medical DIC. These findings should inspire the development of additional tools aimed at providing evidence-based and reliable electronic health record services for secondary use. Conclusions: The research method outlined for the proof-of-concept provenance class has been crafted to promote effective and reliable core data management practices. It aims to enhance biomedical data by imbuing it with meaningful provenance, thereby bolstering the benefits for both research and society. Additionally, it facilitates the streamlined reuse of biomedical data. As a result, the system mitigates risks, as data analysis without knowledge of the origin and quality of all data elements is rendered futile. While the approach was initially developed for the medical DIC use case, these principles can be universally applied throughout the scientific domain.
CRIS-Team Scopus Importer:2024-12-16
Keywords: data integration center; data integrity; data management; medical informatics; metadata; provenance; traceability

Transfer learning for medical image classification: a literature review - Kim, Hee E.; Cosa-Linan, Alejandro; Santhanam, Nandhini; Jannesari, Mahboubeh; Maros, Mate E.; Ganslandt, Thomas (BMC Medical Imaging, 2022)
BACKGROUND: Transfer learning (TL) with convolutional neural networks aims to improve performances on a new task by leveraging the knowledge of similar tasks learned in advance. It has made a major contribution to medical image analysis as it overcomes the data scarcity problem as well as it saves time and hardware resources. However, transfer learning has been arbitrarily configured in the majority of studies. This review paper attempts to provide guidance for selecting a model and TL approaches for the medical image classification task. METHODS: 425 peer-reviewed articles were retrieved from two databases, PubMed and Web of Science, published in English, up until December 31, 2020. Articles were assessed by two independent reviewers, with the aid of a third reviewer in the case of discrepancies. We followed the PRISMA guidelines for the paper selection and 121 studies were regarded as eligible for the scope of this review. We investigated articles focused on selecting backbone models and TL approaches including feature extractor, feature extractor hybrid, fine-tuning and fine-tuning from scratch. RESULTS: The majority of studies (n = 57) empirically evaluated multiple models followed by deep models (n = 33) and shallow (n = 24) models. Inception, one of the deep models, was the most employed in literature (n = 26). With respect to the TL, the majority of studies (n = 46) empirically benchmarked multiple approaches to identify the optimal configuration. The rest of the studies applied only a single approach for which feature extractor (n = 38) and fine-tuning from scratch (n = 27) were the two most favored approaches. Only a few studies applied feature extractor hybrid (n = 7) and fine-tuning (n = 3) with pretrained models. CONCLUSION: The investigated studies demonstrated the efficacy of transfer learning despite the data scarcity. We encourage data scientists and practitioners to use deep models (e.g. ResNet or Inception) as feature extractors, which can save computational costs and time without degrading the predictive power.
CRIS-Team Scopus Importer:2022-04-22
Keywords: Convolutional neural network; Deep learning; Fine-tuning; Medical image analysis; Transfer learning

Using Machine Learning and Feature Importance to Identify Risk Factors for Mortality in Pediatric Heart Surgery - Kapsner, Lorenz; Feißt, Manuel; Purbojo, Ariawan; Prokosch, Hans-Ulrich; Ganslandt, Thomas; Dittrich, Sven; Mang, Jonathan M.; Wällisch, Wolfgang (Diagnostics, 2024)
Background: The objective of this IRB-approved retrospective monocentric study was to identify risk factors for mortality after surgery for congenital heart defects (CHDs) in pediatric patients using machine learning (ML). CHD belongs to the most common congenital malformations, and remains the leading mortality cause from birth defects. Methods: The most recent available hospital encounter for each patient with an age <18 years hospitalized for CHD-related cardiac surgery between the years 2011 and 2020 was included in this study. The cohort consisted of 1302 eligible patients (mean age [SD]: 402.92 [±562.31] days), who were categorized into four disease groups. A random survival forest (RSF) and the ‘eXtreme Gradient Boosting’ algorithm (XGB) were applied to model mortality (incidence: 5.6% [n = 73 events]). All models were then applied to predict the outcome in an independent holdout test dataset (40% of the cohort). Results: RSF and XGB achieved average C-indices of 0.85 (±0.01) and 0.79 (±0.03), respectively. Feature importance was assessed with ‘SHapley Additive exPlanations’ (SHAP) and ‘Time-dependent explanations of machine learning survival models’ (SurvSHAP(t)), both of which revealed high importance of the maximum values of serum creatinine observed within 72 h post-surgery for both ML methods. Conclusions: ML methods, along with model explainability tools, can reveal interesting insights into mortality risk after surgery for CHD. The proposed analytical workflow can serve as a blueprint for translating the analysis into a federated setting that builds upon the infrastructure of the German Medical Informatics Initiative.
CRIS-Team Scopus Importer:2024-12-06
Keywords: congenital heart defects (CHDs); eXtreme Gradient Boosting (XGB); feature importance; machine learning (ML); mortality; random survival forest (RSF); risk factors

Visualization Techniques of Time-Oriented Data for the Comparison of Single Patients With Multiple Patients or Cohorts: Scoping Review - Scheer, Jan; Volkert, Alisa; Brich, Nicolas; Weinert, Lina; Santhanam, Nandhini; Krone, Michael; Ganslandt, Thomas; Boeker, Martin; Nagel, Till (Journal of Medical Internet Research, 2022)
BACKGROUND: Visual analysis and data delivery in the form of visualizations are of great importance in health care, as such forms of presentation can reduce errors and improve care and can also help provide new insights into long-term disease progression. Information visualization and visual analytics also address the complexity of long-term, time-oriented patient data by reducing inherent complexity and facilitating a focus on underlying and hidden patterns. OBJECTIVE: This review aims to provide an overview of visualization techniques for time-oriented data in health care, supporting the comparison of patients. We systematically collected literature and report on the visualization techniques supporting the comparison of time-based data sets of single patients with those of multiple patients or their cohorts and summarized the use of these techniques. METHODS: This scoping review used the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) checklist. After all collected articles were screened by 16 reviewers according to the criteria, 6 reviewers extracted the set of variables under investigation. The characteristics of these variables were based on existing taxonomies or identified through open coding. RESULTS: Of the 249 screened articles, we identified 22 (8.8%) that fit all criteria and reviewed them in depth. We collected and synthesized findings from these articles for medical aspects such as medical context, medical objective, and medical data type, as well as for the core investigated aspects of visualization techniques, interaction techniques, and supported tasks. The extracted articles were published between 2003 and 2019 and were mostly situated in clinical research. These systems used a wide range of visualization techniques, most frequently showing changes over time. Timelines and temporal line charts occurred 8 times each, followed by histograms with 7 occurrences and scatterplots with 5 occurrences. We report on the findings quantitatively through visual summarization, as well as qualitatively. CONCLUSIONS: The articles under review in general mitigated complexity through visualization and supported diverse medical objectives. We identified 3 distinct patient entities: single patients, multiple patients, and cohorts. Cohorts were typically visualized in condensed form, either through prior data aggregation or through visual summarization, whereas visualization of individual patients often contained finer details. All the systems provided mechanisms for viewing and comparing patient data. However, explicitly comparing a single patient with multiple patients or a cohort was supported only by a few systems. These systems mainly use basic visualization techniques, with some using novel visualizations tailored to a specific task. Overall, we found the visual comparison of measurements between single and multiple patients or cohorts to be underdeveloped, and we argue for further research in a systematic review, as well as the usefulness of a design space.
CRIS-Team Scopus Importer:2022-11-04
Keywords: cohorts; comparison; information visualization; multiple patients; patient data; single patients; time-oriented data; visual analytics; visualization systems