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000272953 1001_ $$aHeumos, Lukas$$b0
000272953 245__ $$aAn open-source framework for end-to-end analysis of electronic health record data.
000272953 260__ $$aNew York, NY$$bNature America Inc.$$c2024
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000272953 520__ $$aWith progressive digitalization of healthcare systems worldwide, large-scale collection of electronic health records (EHRs) has become commonplace. However, an extensible framework for comprehensive exploratory analysis that accounts for data heterogeneity is missing. Here we introduce ehrapy, a modular open-source Python framework designed for exploratory analysis of heterogeneous epidemiology and EHR data. ehrapy incorporates a series of analytical steps, from data extraction and quality control to the generation of low-dimensional representations. Complemented by rich statistical modules, ehrapy facilitates associating patients with disease states, differential comparison between patient clusters, survival analysis, trajectory inference, causal inference and more. Leveraging ontologies, ehrapy further enables data sharing and training EHR deep learning models, paving the way for foundational models in biomedical research. We demonstrate ehrapy's features in six distinct examples. We applied ehrapy to stratify patients affected by unspecified pneumonia into finer-grained phenotypes. Furthermore, we reveal biomarkers for significant differences in survival among these groups. Additionally, we quantify medication-class effects of pneumonia medications on length of stay. We further leveraged ehrapy to analyze cardiovascular risks across different data modalities. We reconstructed disease state trajectories in patients with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) based on imaging data. Finally, we conducted a case study to demonstrate how ehrapy can detect and mitigate biases in EHR data. ehrapy, thus, provides a framework that we envision will standardize analysis pipelines on EHR data and serve as a cornerstone for the community.
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000272953 650_2 $$2MeSH$$aHumans
000272953 650_2 $$2MeSH$$aElectronic Health Records
000272953 650_2 $$2MeSH$$aCOVID-19: epidemiology
000272953 650_2 $$2MeSH$$aSARS-CoV-2
000272953 650_2 $$2MeSH$$aPneumonia: epidemiology
000272953 650_2 $$2MeSH$$aDeep Learning
000272953 7001_ $$aEhmele, Philipp$$b1
000272953 7001_ $$aTreis, Tim$$b2
000272953 7001_ $$00000-0002-0966-4458$$aUpmeier Zu Belzen, Julius$$b3
000272953 7001_ $$aRoellin, Eljas$$b4
000272953 7001_ $$aMay, Lilly$$b5
000272953 7001_ $$aNamsaraeva, Altana$$b6
000272953 7001_ $$aHorlava, Nastassya$$b7
000272953 7001_ $$00000-0002-1960-8812$$aShitov, Vladimir A$$b8
000272953 7001_ $$00000-0003-4806-4049$$aZhang, Xinyue$$b9
000272953 7001_ $$00000-0001-7744-8565$$aZappia, Luke$$b10
000272953 7001_ $$0P:(DE-2719)9000620$$aKnoll, Rainer$$b11$$udzne
000272953 7001_ $$aLang, Niklas J$$b12
000272953 7001_ $$aHetzel, Leon$$b13
000272953 7001_ $$aVirshup, Isaac$$b14
000272953 7001_ $$00000-0001-9686-6295$$aSikkema, Lisa$$b15
000272953 7001_ $$aCurion, Fabiola$$b16
000272953 7001_ $$aEils, Roland$$b17
000272953 7001_ $$aSchiller, Herbert B$$b18
000272953 7001_ $$aHilgendorff, Anne$$b19
000272953 7001_ $$00000-0002-2419-1943$$aTheis, Fabian J$$b20
000272953 773__ $$0PERI:(DE-600)1484517-9$$a10.1038/s41591-024-03214-0$$gVol. 30, no. 11, p. 3369 - 3380$$n11$$p3369 - 3380$$tNature medicine$$v30$$x1078-8956$$y2024
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