Exploratory electronic health record analysis with ehrapy

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 end-to-end analysis of heterogeneous epidemiology and electronic health record 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 demonstrated ehrapys features in five distinct examples: We first applied ehrapy to stratify patients affected by unspecified pneumonia into finer-grained phenotypes. Furthermore, we revealed 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. Finally, we reconstructed disease state trajectories in SARS-CoV-2 patients based on imaging data. Ehrapy thus provides a framework that we envision will standardize analysis pipelines on EHR data and serve as a cornerstone for the community. ### Competing Interest Statement LH is an employee of LaminLabs. FJT consults for Immunai Inc., Singularity Bio B.V., CytoReason Ltd, and Omniscope Ltd, and has ownership interest in Dermagnostix GmbH and Cellarity. ### Funding Statement This work was supported by the German Center for Lung Research (DZL), the Helmholtz association and the CRC/TRR 359 Perinatal Development of Immune Cell Topology (PILOT). N.H. and F.J.T. acknowledge support from the German Federal Ministry of Education and Research (BMBF) (LODE, 031L0210A). Co-funded by the European Union (ERC, DeepCell - 101054957). ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: The study used only openly available human data. See: Physionet provides access to the PIC database at https://physionet.org/content/picdb/1.1.0 for credentialed users. The BrixIA images are available at https://github.com/BrixIA/Brixia-score-COVID-19. The diabetic retinopathy dataset is available at https://www.kaggle.com/c/diabetic-retinopathy-detection/data. The data used in this study were obtained from the UK Biobank (www.ukbiobank.ac.uk). Access to the UK Biobank resource was granted under application number 49966. The data are available to researchers upon application to the UK Biobank in accordance with their data access policies and procedures. I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes Physionet provides access to the PIC database at https://physionet.org/content/picdb/1.1.0 for credentialed users. The BrixIA images are available at https://github.com/BrixIA/Brixia-score-COVID-19. The diabetic retinopathy dataset is available at https://www.kaggle.com/c/diabetic-retinopathy-detection/data. The data used in this study were obtained from the UK Biobank (www.ukbiobank.ac.uk). Access to the UK Biobank resource was granted under application number 49966. The data are available to researchers upon application to the UK Biobank in accordance with their data access policies and procedures.