A framework for criteria-based selection and processing of fast healthcare interoperability resources (FHIR) data for statistical analysis: Design and implementation study - Gründner, Julian; Gulden, Christian; Kampf, Marvin; Mate, Sebastian; Prokosch, Hans-Ulrich; Zierk, Jakob (JMIR Medical Informatics, 2021)
Background: The harmonization and standardization of digital medical information for research purposes is a challenging and ongoing collaborative effort. Current research data repositories typically require extensive efforts in harmonizing and transforming original clinical data. The Fast Healthcare Interoperability Resources (FHIR) format was designed primarily to represent clinical processes; therefore, it closely resembles the clinical data model and is more widely available across modern electronic health records. However, no common standardized data format is directly suitable for statistical analyses, and data need to be preprocessed before statistical analysis. Objective: This study aimed to elucidate how FHIR data can be queried directly with a preprocessing service and be used for statistical analyses. Methods: We propose that the binary JavaScript Object Notation format of the PostgreSQL (PSQL) open source database is suitable for not only storing FHIR data, but also extending it with preprocessing and filtering services, which directly transform data stored in FHIR format into prepared data subsets for statistical analysis. We specified an interface for this preprocessor, implemented and deployed it at University Hospital Erlangen-Nürnberg, generated 3 sample data sets, and analyzed the available data. Results: We imported real-world patient data from 2016 to 2018 into a standard PSQL database, generating a dataset of approximately 35.5 million FHIR resources, including “Patient,” “Encounter,” “Condition” (diagnoses specified using International Classification of Diseases codes), “Procedure,” and “Observation” (laboratory test results). We then integrated the developed preprocessing service with the PSQL database and the locally installed web-based KETOS analysis platform. Advanced statistical analyses were feasible using the developed framework using 3 clinically relevant scenarios (data-driven establishment of hemoglobin reference intervals, assessment of anemia prevalence in patients with cancer, and investigation of the adverse effects of drugs). Conclusions: This study shows how the standard open source database PSQL can be used to store FHIR data and be integrated with a specifically developed preprocessing and analysis framework. This enables dataset generation with advanced medical criteria and the integration of subsequent statistical analysis. The web-based preprocessing service can be deployed locally at the hospital level, protecting patients’ privacy while being integrated with existing open source data analysis tools currently being developed across Germany.
CRIS-Team Scopus Importer:2021-04-16
Keywords: Data analysis; Data harmonization; Data repositories; Data science; Data standardization; Digital medical information; EHealth; Fast healthcare interoperability resources; HL7 FHIR; Medical information; Patient privacy

A method for the graphical modeling of relative temporal constraints - Mate, Sebastian; Bürkle, Thomas; Kapsner, Lorenz; Toddenroth, Dennis; Kampf, Marvin; Sedlmayr, Martin; Castellanos, Ixchel; Prokosch, Hans-Ulrich; Kraus, Stefan (Journal of Biomedical Informatics, 2019)
Searching for patient cohorts in electronic patient data often requires the definition of temporal constraints between the selection criteria. However, beyond a certain degree of temporal complexity, the non-graphical, form-based approaches implemented in current translational research platforms may be limited when modeling such constraints. In our opinion, there is a need for an easily accessible and implementable, fully graphical method for creating temporal queries. We aim to respond to this challenge with a new graphical notation. Based on Allen's time interval algebra, it allows for modeling temporal queries by arranging simple horizontal bars depicting symbolic time intervals. To make our approach applicable to complex temporal patterns, we apply two extensions: with duration intervals, we enable the inference about relative temporal distances between patient events, and with time interval modifiers, we support counting and excluding patient events, as well as constraining numeric values. We describe how to generate database queries from this notation. We provide a prototypical implementation, consisting of a temporal query modeling frontend and an experimental backend that connects to an i2b2 system. We evaluate our modeling approach on the MIMIC-III database to demonstrate that it can be used for modeling typical temporal phenotyping queries. Data availability statement The source code (licensed under the GNU General Public License v3.0) is available on GitHub (https://github.com/sebmate/AllenGUI and https://github.com/sebmate/AllenSPARQL). Also supplied are the development i2b2 dataset (with the 13 artificial patients) and the SQL scripts to populate the i2b2 system with the MIMIC-III data. Please note that the software is of prototypical character and provided “as is”, without any warranties.
CRIS-Team Scopus Importer:2019-11-15
Keywords: Data integration; Data retrieval; Patient cohort identification; Phenotyping; Phenotyping algorithms; Temporal queries

A Multi-User Terminology Mapping Toolbox - Mate, Sebastian; Seuchter, Susanne A.; Ehrenberg, Katharina; Deppenwiese, Noemi; Zierk, Jakob; Prokosch, Hans-Ulrich; Kraska, Detlef; Kapsner, Lorenz (German Medical Data Sciences: Bringing Data to Life, 2021)
Semantic interoperability is a major challenge in multi-center data sharing projects, a challenge that the German Initiative for Medical Informatics is taking up. With respect to laboratory data, enriching site-specific tests and measurements with LOINC codes appears to be a crucial step in supporting cross-institutional research. However, this effort is very time-consuming, as it requires expert knowledge of local site specifics. To ease this process, we developed a generic manual collaborative terminology mapping tool, the MIRACUM Mapper. It allows the creation of arbitrary mapping workflows involving different user roles. A mapping workflow with two user roles has been implemented at University Hospital Erlangen to support the local LOINC mapping. Additionally, the MIRACUM LabVisualizeR provides summary statistics and visualizations of analyte data. We developed a toolbox that facilitates the collaborative creation of mappings and streamlines the review as well as the validation process. The two tools are available under an open source license.
CRIS-Team Scopus Importer:2021-06-11
Keywords: Interoperability; Laboratory; LOINC; Secondary Use; Terminology

BiasCorrector: Fast and accurate correction of all types of experimental biases in quantitative DNA methylation data derived by different technologies - Kapsner, Lorenz; Zavgorodnij, Mikhail G.; Majorova, Svetlana P.; Hotz-Wagenblatt, Agnes; Kolychev, Oleg V.; Lebedev, Igor N.; Hoheisel, Jörg D.; Hartmann, Arndt; Bauer, Andrea; Mate, Sebastian; Prokosch, Hans-Ulrich; Haller, Florian; et al. (International Journal of Cancer, 2021)
Quantification of DNA methylation in neoplastic cells is crucial both from mechanistic and diagnostic perspectives. However, such measurements are prone to different experimental biases. Polymerase chain reaction (PCR) bias results in an unequal recovery of methylated and unmethylated alleles at the sample preparation step. Post-PCR biases get introduced additionally by the readout processes. Correcting the biases is more practicable than optimising experimental conditions, as demonstrated previously. However, utilisation of our earlier developed algorithm strongly necessitates automation. Here, we present two R packages: rBiasCorrection, the core algorithms to correct biases; and BiasCorrector, its web-based graphical user interface frontend. The software detects and analyses experimental biases in calibration DNA samples at a single base resolution by using cubic polynomial and hyperbolic regression. The correction coefficients from the best regression type are employed to compensate for the bias. Three common technologies—bisulphite pyrosequencing, next-generation sequencing and oligonucleotide microarrays—were used to comprehensively test BiasCorrector. We demonstrate the accuracy of BiasCorrector's performance and reveal technology-specific PCR- and post-PCR biases. BiasCorrector effectively eliminates biases regardless of their nature, locus, the number of interrogated methylation sites and the detection method, thus representing a user-friendly tool for producing accurate epigenetic results.
CRIS-Team Scopus Importer:2021-06-04
Keywords: BiasCorrector; cancer; DNA methylation; PCR-bias; post-PCR bias

Extending the Minimum Information About BIobank Data Sharing Terminology to Describe Samples, Sample Donors, and Events - Eklund, Niina; Andrianarisoa, Ny Haingo; Van Enckevort, Esther; Anton, Gabriele; Debucquoy, Annelies; Mueller, Heimo; Zaharenko, Linda; Engels, Caecilia; Ebert, Lars; Neumann, Michael; Geeraert, Joachim; T'Joen, Veronique; et al. (Biopreservation and biobanking, 2020)
Introduction: The Minimum Information About BIobank data Sharing (MIABIS) was initiated in 2012. MIABIS aims to create a common biobank terminology to facilitate data sharing in biobanks and sample collections. The MIABIS Core terminology consists of three components describing biobanks, sample collections, and studies, in which information on samples and sample donors is provided at aggregated form. However, there is also a need to describe samples and sample donors at an individual level to allow more elaborate queries on available biobank samples and data. Therefore the MIABIS terminology has now been extended with components describing samples and sample donors at an individual level.
CRIS-Team WoS Importer:2020-05-02

From a Content Delivery Portal to a Knowledge Management System for Standardized Cancer Documentation - Schlue, Danijela; Mate, Sebastian; Haier, Jörg; Kadioglu, Dennis; Prokosch, Hans-Ulrich; Breil, Bernhard (Studies in health technology and informatics, 2017)
Heterogeneous tumor documentation and its challenges of interpretation of medical terms lead to problems in analyses of data from clinical and epidemiological cancer registries. The objective of this project was to design, implement and improve a national content delivery portal for oncological terms. Data elements of existing handbooks and documentation sources were analyzed, combined and summarized by medical experts of different comprehensive cancer centers. Informatics experts created a generic data model based on an existing metadata repository. In order to establish a national knowledge management system for standardized cancer documentation, a prototypical tumor wiki was designed and implemented. Requirements engineering techniques were applied to optimize this platform. It is targeted to user groups such as documentation officers, physicians and patients. The linkage to other information sources like PubMed and MeSH was realized.
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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

Investigating the Capabilities of FHIR Search for Clinical Trial Phenotyping - Gulden, Christian; Mate, Sebastian; Prokosch, Hans-Ulrich; Kraus, Stefan (Studies in health technology and informatics, 2018)
Clinical trials are the foundation of evidence-based medicine and their computerized support has been a recurring theme in medical informatics. One challenging aspect is the representation of eligibility criteria in a machine-readable format to automate the identification of suitable participants. In this study, we investigate the capabilities for expressing trial eligibility criteria via the search functionality specified in HL7 FHIR, an emerging standard for exchanging healthcare information electronically which also defines a set of operations for searching for health record data. Using a randomly sampled subset of 303 eligibility criteria from ClinicalTrials.gov yielded a 34 % success rate in representing them using the FHIR search semantics. While limitations are present, the FHIR search semantics are a viable tool for supporting preliminary trial eligibility assessment.
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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

Ketos: Clinical decision support and machine learning as a service – A training and deployment platform based on Docker, OMOP-CDM, and FHIR Web Services - Gründner, Julian; Schwachhofer, Thorsten; Sippl, Phillip; Wolf, Nicolas; Erpenbeck, Marcel; Gulden, Christian; Kapsner, Lorenz A.; Zierk, Jakob; Mate, Sebastian; Stürzl, Michael; Croner, Roland; Prokosch, Hans-Ulrich; et al. (PLoS ONE, 2019)
Background and objective To take full advantage of decision support, machine learning, and patient-level prediction models, it is important that models are not only created, but also deployed in a clinical setting. The KETOS platform demonstrated in this work implements a tool for researchers allowing them to perform statistical analyses and deploy resulting models in a secure environment. Methods The proposed system uses Docker virtualization to provide researchers with reproducible data analysis and development environments, accessible via Jupyter Notebook, to perform statistical analysis and develop, train and deploy models based on standardized input data. The platform is built in a modular fashion and interfaces with web services using the Health Level 7 (HL7) Fast Healthcare Interoperability Resources (FHIR) standard to access patient data. In our prototypical implementation we use an OMOP common data model (OMOP-CDM) database. The architecture supports the entire research lifecycle from creating a data analysis environment, retrieving data, and training to final deployment in a hospital setting. Results We evaluated the platform by establishing and deploying an analysis and end user application for hemoglobin reference intervals within the University Hospital Erlangen. To demonstrate the potential of the system to deploy arbitrary models, we loaded a colorectal cancer dataset into an OMOP database and built machine learning models to predict patient outcomes and made them available via a web service. We demonstrated both the integration with FHIR as well as an example end user application. Finally, we integrated the platform with the open source DataSHIELD architecture to allow for distributed privacy preserving data analysis and training across networks of hospitals. Conclusion The KETOS platform takes a novel approach to data analysis, training and deploying decision support models in a hospital or healthcare setting. It does so in a secure and privacy-preserving manner, combining the flexibility of Docker virtualization with the advantages of standardized vocabularies, a widely applied database schema (OMOP-CDM), and a standardized way to exchange medical data (FHIR).
CRIS-Team Scopus Importer:2019-10-11

Linking a Consortium-Wide Data Quality Assessment Tool with the MIRACUM Metadata Repository - Kapsner, Lorenz; Mang, Jonathan M.; Mate, Sebastian; Seuchter, Susanne A.; Vengadeswaran, Abishaa; Bathelt, Franziska; Deppenwiese, Noemi; Kadioglu, Dennis; Kraska, Detlef; Prokosch, Hans-Ulrich (Applied Clinical Informatics, 2021)
Background Many research initiatives aim at using data from electronic health records (EHRs) in observational studies. Participating sites of the German Medical Informatics Initiative (MII) established data integration centers to integrate EHR data within research data repositories to support local and federated analyses. To address concerns regarding possible data quality (DQ) issues of hospital routine data compared with data specifically collected for scientific purposes, we have previously presented a data quality assessment (DQA) tool providing a standardized approach to assess DQ of the research data repositories at the MIRACUM consortium's partner sites.
CRIS-Team WoS Importer:2021-09-03

Mapping the Entire Record-An Alternative Approach to Data Access from Medical Logic Modules - Kraus, Stefan; Toddenroth, Dennis; Staudigel, Martin; Rödle, Wolfgang; Unberath, Philipp; Griebel, Lena; Prokosch, Hans-Ulrich; Mate, Sebastian (Applied Clinical Informatics, 2020)
OBJECTIVES: This study aimed to describe an alternative approach for accessing electronic medical records (EMRs) from clinical decision support (CDS) functions based on Arden Syntax Medical Logic Modules, which can be paraphrased as "map the entire record." METHODS: Based on an experimental Arden Syntax processor, we implemented a method to transform patient data from a commercial patient data management system (PDMS) to tree-structured documents termed CDS EMRs. They are encoded in a specific XML format that can be directly transformed to Arden Syntax data types by a mapper natively integrated into the processor. The internal structure of a CDS EMR reflects the tabbed view of an EMR in the graphical user interface of the PDMS. RESULTS: The study resulted in an architecture that provides CDS EMRs in the form of a network service. The approach enables uniform data access from all Medical Logic Modules and requires no mapping parameters except a case number. Measurements within a CDS EMR can be addressed with straightforward path expressions. The approach is in routine use at a German university hospital for more than 2 years. CONCLUSION: This practical approach facilitates the use of CDS functions in the clinical routine at our local hospital. It is transferrable to standard-compliant Arden Syntax processors with moderate effort. Its comprehensibility can also facilitate teaching and development. Moreover, it may lower the entry barrier for the application of the Arden Syntax standard and could therefore promote its dissemination.
CRIS-Team Scopus Importer:2020-05-22

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

On-The-Fly Query Translation Between i2b2 and Samply in the German Biobank Node (GBN) Prototypes - Mate, Sebastian; Vormstein, Patric; Kadioglu, Dennis; Majeed, Raphael W; Lablans, Martin; Prokosch, Hans-Ulrich; Storf, Holger (Studies in health technology and informatics, 2017)
Information retrieval is a major challenge in medical informatics. Various research projects have worked on this task in recent years on an institutional level by developing tools to integrate and retrieve information. However, when it comes down to querying such data across institutions, the challenge persists due to the high heterogeneity of data and differences in software systems. The German Biobank Node (GBN) project faced this challenge when trying to interconnect four biobanks to enable distributed queries for biospecimens. All biobanks had already established integrated data repositories, and some of them were already part of research networks. Instead of developing another software platform, GBN decided to form a bridge between these. This paper describes and discusses a core component from the GBN project, the OmniQuery library, which was implemented to enable on-the-fly query translation between heterogeneous research infrastructures.
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Pan-European Data Harmonization for Biobanks in ADOPT BBMRI-ERIC - Mate, Sebastian; Kampf, Marvin; Rödle, Wolfgang; Kraus, Stefan; Proynova, Rumyana; Silander, Kaisa; Ebert, Lars; Lablans, Martin; Schüttler, Christina; Knell, Christian; Eklund, Niina; Hummel, Michael; et al. (Applied Clinical Informatics, 2019)
BACKGROUND: High-quality clinical data and biological specimens are key for medical research and personalized medicine. The Biobanking and Biomolecular Resources Research Infrastructure-European Research Infrastructure Consortium (BBMRI-ERIC) aims to facilitate access to such biological resources. The accompanying ADOPT BBMRI-ERIC project kick-started BBMRI-ERIC by collecting colorectal cancer data from European biobanks. OBJECTIVES: To transform these data into a common representation, a uniform approach for data integration and harmonization had to be developed. This article describes the design and the implementation of a toolset for this task. METHODS: Based on the semantics of a metadata repository, we developed a lexical bag-of-words matcher, capable of semiautomatically mapping local biobank terms to the central ADOPT BBMRI-ERIC terminology. Its algorithm supports fuzzy matching, utilization of synonyms, and sentiment tagging. To process the anonymized instance data based on these mappings, we also developed a data transformation application. RESULTS: The implementation was used to process the data from 10 European biobanks. The lexical matcher automatically and correctly mapped 78.48% of the 1,492 local biobank terms, and human experts were able to complete the remaining mappings. We used the expert-curated mappings to successfully process 147,608 data records from 3,415 patients. CONCLUSION: A generic harmonization approach was created and successfully used for cross-institutional data harmonization across 10 European biobanks. The software tools were made available as open source.
CRIS-Team Scopus Importer:2019-09-20

Patient Cohort Identification on Time Series Data Using the OMOP Common Data Model - Maier, Christian; Kapsner, Lorenz; Mate, Sebastian; Prokosch, Hans-Ulrich; Kraus, Stefan (Applied Clinical Informatics, 2021)
Background â The identification of patient cohorts for recruiting patients into clinical trials requires an evaluation of study-specific inclusion and exclusion criteria. These criteria are specified depending on corresponding clinical facts. Some of these facts may not be present in the clinical source systems and need to be calculated either in advance or at cohort query runtime (so-called feasibility query). Objectives â We use the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) as the repository for our clinical data. However, Atlas, the graphical user interface of OMOP, does not offer the functionality to perform calculations on facts data. Therefore, we were in search for a different approach. The objective of this study is to investigate whether the Arden Syntax can be used for feasibility queries on the OMOP CDM to enable on-The-fly calculations at query runtime, to eliminate the need to precalculate data elements that are involved with researchers' criteria specification. Methods â We implemented a service that reads the facts from the OMOP repository and provides it in a form which an Arden Syntax Medical Logic Module (MLM) can process. Then, we implemented an MLM that applies the eligibility criteria to every patient data set and outputs the list of eligible cases (i.e., performs the feasibility query). Results â The study resulted in an MLM-based feasibility query that identifies cases of overventilation as an example of how an on-The-fly calculation can be realized. The algorithm is split into two MLMs to provide the reusability of the approach. Conclusion â We found that MLMs are a suitable technology for feasibility queries on the OMOP CDM. Our method of performing on-The-fly calculations can be employed with any OMOP instance and without touching existing infrastructure like the Extract, Transform and Load pipeline. Therefore, we think that it is a well-suited method to perform on-The-fly calculations on OMOP.
CRIS-Team Scopus Importer:2021-02-12
Keywords: Arden Syntax; clinical data; electronic health records; information storage and retrieval; Medical Logic Modules

Proof-of-Concept Integration of Heterogeneous Biobank IT Infrastructures into a Hybrid Biobanking Network - Mate, Sebastian; Kadioglu, Dennis; Majeed, Raphael W; Stöhr, Mark R; Folz, Michael; Vormstein, Patric; Storf, Holger; Brucker, Daniel P; Keune, Dietmar; Zerbe, Norman; Hummel, Michael; Senghas, Karsten; et al. (Studies in health technology and informatics, 2017)
Cross-institutional biobank networks hold the promise of supporting medicine by enabling the exchange of associated samples for research purposes. Various initiatives, such as BBMRI-ERIC and German Biobank Node (GBN), aim to interconnect biobanks for enabling the compilation of joint biomaterial collections. However, building software platforms to facilitate such collaboration is challenging due to the heterogeneity of existing biobank IT infrastructures and the necessary efforts for installing and maintaining additional software components. As a remedy, this paper presents the concept of a hybrid network for interconnecting already existing software components commonly found in biobanks and a proof-of-concept implementation of two prototypes involving four biobanks of the German Biobank Node. Here we demonstrate the successful bridging of two IT systems found in many German biobanks - Samply and i2b2.
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Reduced Rate of Inpatient Hospital Admissions in 18 German University Hospitals During the COVID-19 Lockdown - Kapsner, Lorenz; Kampf, Marvin; Seuchter, Susanne A.; Gründner, Julian; Gulden, Christian; Mate, Sebastian; Mang, Jonathan M.; Schüttler, Christina; Deppenwiese, Noemi; Krause, Linda; Zöller, Daniela; Balig, Julien; et al. (Frontiers in Public Health, 2021)
The COVID-19 pandemic has caused strains on health systems worldwide disrupting routine hospital services for all non-COVID patients. Within this retrospective study, we analyzed inpatient hospital admissions across 18 German university hospitals during the 2020 lockdown period compared to 2018. Patients admitted to hospital between January 1 and May 31, 2020 and the corresponding periods in 2018 and 2019 were included in this study. Data derived from electronic health records were collected and analyzed using the data integration center infrastructure implemented in the university hospitals that are part of the four consortia funded by the German Medical Informatics Initiative. Admissions were grouped and counted by ICD 10 chapters and specific reasons for treatment at each site. Pooled aggregated data were centrally analyzed with descriptive statistics to compare absolute and relative differences between time periods of different years. The results illustrate how care process adoptions depended on the COVID-19 epidemiological situation and the criticality of the disease. Overall inpatient hospital admissions decreased by 35% in weeks 1 to 4 and by 30.3% in weeks 5 to 8 after the lockdown announcement compared to 2018. Even hospital admissions for critical care conditions such as malignant cancer treatments were reduced. We also noted a high reduction of emergency admissions such as myocardial infarction (38.7%), whereas the reduction in stroke admissions was smaller (19.6%). In contrast, we observed a considerable reduction in admissions for non-critical clinical situations, such as hysterectomies for benign tumors (78.8%) and hip replacements due to arthrosis (82.4%). In summary, our study shows that the university hospital admission rates in Germany were substantially reduced following the national COVID-19 lockdown. These included critical care or emergency conditions in which deferral is expected to impair clinical outcomes. Future studies are needed to delineate how appropriate medical care of critically ill patients can be maintained during a pandemic.
CRIS-Team Scopus Importer:2021-02-05
Keywords: COVID-19; Germany; healthcare systems; inpatient hospital admissions; lockdown; medical informatics initiative; pandemic; university hospitals

Samply.MDR - A Metadata Repository and Its Application in Various Research Networks - Kadioglu, Dennis; Breil, Bernhard; Knell, Christian; Lablans, Martin; Mate, Sebastian; Schlue, Danijela; Serve, Hubert; Storf, Holger; Ückert, Frank; Wagner, Thomas; Weingardt, Paul; Prokosch, Hans-Ulrich (Studies in health technology and informatics, 2018)
Collaboration in medical research is becoming common, especially for collecting relevant cases across institutional boundaries. If the data, which is usually very heterogeneously formalized and structured, can be integrated, such a collaboration can facilitate research. An absolute prerequisite for this is an extensive description about the formalization and exact meaning of every data element contained in a dataset. This information is commonly known as metadata. Various research networking projects tackle this challenge with the development of concepts and IT tools. The Samply Metadata Repository (Samply.MDR) is a solution for managing and publishing such metadata in a standardized and reusable way. In this article we present the structure and features of the Samply.MDR as well as its flexible usability by giving an overview about its application in various projects.
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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

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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Strategies for biobank networks : Classification of different approaches for locating samples and an outlook on the future within the BBMRI-ERIC - Lablans, Martin; Kadioglu, Dennis; Mate, Sebastian; Leb, Ines; Prokosch, Hans-Ulrich; Ueckert, Frank (Bundesgesundheitsblatt, Gesundheitsforschung, Gesundheitsschutz, 2016)
Medical research projects often require more biological material than can be supplied by a single biobank. For this reason, a multitude of strategies support locating potential research partners with matching material without requiring centralization of sample storage.Classification of different strategies for biobank networks, in particular for locating suitable samples. Description of an IT infrastructure combining these strategies.Existing strategies can be classified according to three criteria: (a) granularity of sample data: coarse bank-level data (catalogue) vs. fine-granular sample-level data, (b) location of sample data: central (central search service) vs. decentral storage (federated search services), and (c) level of automation: automatic (query-based, federated search service) vs. semi-automatic (inquiry-based, decentral search). All mentioned search services require data integration. Metadata help to overcome semantic heterogeneity.The "Common Service IT" in BBMRI-ERIC (Biobanking and BioMolecular Resources Research Infrastructure) unites a catalogue, the decentral search and metadata in an integrated platform. As a result, researchers receive versatile tools to search suitable biomaterial, while biobanks retain a high degree of data sovereignty.Despite their differences, the presented strategies for biobank networks do not rule each other out but can complement and even benefit from each other.
EVALuna2:149

The COVID-19 Data Exchange Platform of the German University Medicine - Prokosch, Hans-Ulrich; Bahls, Thomas; Bialke, Martin; Eils, Jürgen; Fegeler, Christian; Gründner, Julian; Haarbrandt, Birger; Hampf, Christopher; Hoffmann, Wolfgang; Hund, Hauke; Kampf, Marvin; Kapsner, Lorenz; et al. (Challenges of Trustable AI and Added-Value on Health, 2022)
COVID-19 has challenged the healthcare systems worldwide. To quickly identify successful diagnostic and therapeutic approaches large data sharing approaches are inevitable. Though organizational clinical data are abundant, many of them are available only in isolated silos and largely inaccessible to external researchers. To overcome and tackle this challenge the university medicine network (comprising all 36 German university hospitals) has been founded in April 2020 to coordinate COVID-19 action plans, diagnostic and therapeutic strategies and collaborative research activities. 13 projects were initiated from which the CODEX project, aiming at the development of a Germany-wide Covid-19 Data Exchange Platform, is presented in this publication. We illustrate the conceptual design, the stepwise development and deployment, first results and the current status.
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