Lukas Heumos, Philipp Ehmele, Tim Treis, Julius Upmeier zu Belzen, Eljas Roellin, Lilly May, Altana Namsaraeva, Nastassya Horlava, Vladimir A. Shitov, Xinyue Zhang, Luke Zappia, Rainer Knoll, Niklas J. Lang, Leon Hetzel, Isaac Virshup, Lisa Sikkema, Fabiola Curion, Roland Eils, Herbert B. Schiller, Anne Hilgendorff, Fabian J. Theis
{"title":"用于端到端分析电子健康记录数据的开源框架","authors":"Lukas Heumos, Philipp Ehmele, Tim Treis, Julius Upmeier zu Belzen, Eljas Roellin, Lilly May, Altana Namsaraeva, Nastassya Horlava, Vladimir A. Shitov, Xinyue Zhang, Luke Zappia, Rainer Knoll, Niklas J. Lang, Leon Hetzel, Isaac Virshup, Lisa Sikkema, Fabiola Curion, Roland Eils, Herbert B. Schiller, Anne Hilgendorff, Fabian J. Theis","doi":"10.1038/s41591-024-03214-0","DOIUrl":null,"url":null,"abstract":"<p>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.</p>","PeriodicalId":19037,"journal":{"name":"Nature Medicine","volume":null,"pages":null},"PeriodicalIF":58.7000,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"An open-source framework for end-to-end analysis of electronic health record data\",\"authors\":\"Lukas Heumos, Philipp Ehmele, Tim Treis, Julius Upmeier zu Belzen, Eljas Roellin, Lilly May, Altana Namsaraeva, Nastassya Horlava, Vladimir A. Shitov, Xinyue Zhang, Luke Zappia, Rainer Knoll, Niklas J. Lang, Leon Hetzel, Isaac Virshup, Lisa Sikkema, Fabiola Curion, Roland Eils, Herbert B. Schiller, Anne Hilgendorff, Fabian J. Theis\",\"doi\":\"10.1038/s41591-024-03214-0\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>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. 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An open-source framework for end-to-end analysis of electronic health record data
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.
期刊介绍:
Nature Medicine is a monthly journal publishing original peer-reviewed research in all areas of medicine. The publication focuses on originality, timeliness, interdisciplinary interest, and the impact on improving human health. In addition to research articles, Nature Medicine also publishes commissioned content such as News, Reviews, and Perspectives. This content aims to provide context for the latest advances in translational and clinical research, reaching a wide audience of M.D. and Ph.D. readers. All editorial decisions for the journal are made by a team of full-time professional editors.
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