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Heumos, L. ; Ehmele, P. ; Treis, T. ; Upmeier Zu Belzen, J.* ; Roellin, E. ; May, L. ; Namsaraeva, A. ; Horlava, N. ; Shitov, V.A. ; Zhang, X. ; Zappia, L. ; Knöll, R.* ; Lang, N.J. ; Hetzel, L. ; Virshup, I. ; Sikkema, L. ; Curion, F. ; Eils, R.* ; Schiller, H. ; Hilgendorff, A. ; Theis, F.J.

An open-source framework for end-to-end analysis of electronic health record data.

Nat. Med., DOI: 10.1038/s41591-024-03214-0 (2024)
Verlagsversion DOI PMC
Open Access Hybrid
Creative Commons Lizenzvertrag
With progressive digitalization of healthcare systems worldwide, large-scale collection of electronic health records (EHRs) has become commonplace. However, an extensible framework for comprehensive exploratory analysis that accounts for data heterogeneity is missing. Here we introduce ehrapy, a modular open-source Python framework designed for exploratory analysis of heterogeneous epidemiology and EHR data. ehrapy incorporates a series of analytical steps, from data extraction and quality control to the generation of low-dimensional representations. Complemented by rich statistical modules, ehrapy facilitates associating patients with disease states, differential comparison between patient clusters, survival analysis, trajectory inference, causal inference and more. Leveraging ontologies, ehrapy further enables data sharing and training EHR deep learning models, paving the way for foundational models in biomedical research. We demonstrate ehrapy's features in six distinct examples. We applied ehrapy to stratify patients affected by unspecified pneumonia into finer-grained phenotypes. Furthermore, we reveal biomarkers for significant differences in survival among these groups. Additionally, we quantify medication-class effects of pneumonia medications on length of stay. We further leveraged ehrapy to analyze cardiovascular risks across different data modalities. We reconstructed disease state trajectories in patients with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) based on imaging data. Finally, we conducted a case study to demonstrate how ehrapy can detect and mitigate biases in EHR data. ehrapy, thus, provides a framework that we envision will standardize analysis pipelines on EHR data and serve as a cornerstone for the community.
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Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Schlagwörter Clinical-trials; Primary-care; Missing Data; Resource; Common; Associations; Discovery; Multiple; Profile; Models
Sprache englisch
Veröffentlichungsjahr 2024
HGF-Berichtsjahr 2024
ISSN (print) / ISBN 1078-8956
e-ISSN 1546-170X
Zeitschrift Nature medicine
Verlag Nature Publishing Group
Verlagsort New York, NY
Begutachtungsstatus Peer reviewed
Institut(e) Institute of Computational Biology (ICB)
German Center for Lung Research (DZL)
Institute of Lung Health and Immunity (LHI)
POF Topic(s) 30205 - Bioengineering and Digital Health
80000 - German Center for Lung Research
30202 - Environmental Health
Forschungsfeld(er) Enabling and Novel Technologies
Lung Research
PSP-Element(e) G-503800-001
G-503800-004
G-503893-001
G-501800-810
G-501693-001
G-552100-001
Förderungen
Chan Zuckerberg Initiative
Federal Ministry of Education and Research
Konrad Zuse School of Excellence in Learning and Intelligent Systems (ELIZA) through the DAAD
European Union (ERC)
German Federal Ministry of Education and Research (BMBF)
Helmholtz Association
German Center for Lung Research (DZL)
DOI 10.1038/s41591-024-03214-0
WOS ID 001311664900003
Scopus ID 85203984379
PubMed ID 39266748
Erfassungsdatum 2024-10-23
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