Holweg F, Achenbach S, Deppenwiese N, Gaede L, Prokosch HU (2022)
Publication Type: Book chapter / Article in edited volumes
Publication year: 2022
Publisher: IOS Press
Edited Volumes: Healthcare of the Future 2022
Series: Studies in Health Technology and Informatics
Book Volume: 292
Pages Range: 96-99
ISBN: 978-1-64368-281-5
DOI: 10.3233/SHTI220331
Coronary heart disease is among the most frequent causes of death globally. Thus, our research project aims to develop prognostic models, to predict the risk of spontaneous myocardial infarctions based on a combination of clinical parameters and image data sets (invasive coronary angiograms). To train such models we use data from more than 30,000 coronary angiograms acquired at the cardiology department of Erlangen University Hospital. To linking such proprietary data with additional clinical parameters and to harmonize it for future cross-hospital federated machine learning approaches we defined a mapping for coronary angiography based on the symptom/ clinical phenotype HL7® FHIR® module of the German medical informatics initiative. In this paper we describe the final design of the coronary angiography information model and our mapping approach to ICD-10 and SNOMED CT. From the database we use a subset of 15 required values patient characteristics to create the HL7® FHIR® resource.
APA:
Holweg, F., Achenbach, S., Deppenwiese, N., Gaede, L., & Prokosch, H.-U. (2022). Towards a FHIR-Based Data Model for Coronary Angiography Observations. In Thomas Bürkle, Kerstin Denecke, Jürgen Holm, Murat Sariyar, Michael Lehmann (Eds.), Healthcare of the Future 2022. (pp. 96-99). IOS Press.
MLA:
Holweg, Florian, et al. "Towards a FHIR-Based Data Model for Coronary Angiography Observations." Healthcare of the Future 2022. Ed. Thomas Bürkle, Kerstin Denecke, Jürgen Holm, Murat Sariyar, Michael Lehmann, IOS Press, 2022. 96-99.
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