von 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
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.
Nature medicine [New York, NY] : Springer Nature, 1995 30(2024), 11, Seite 3369-3380 Online-Ressource
von Julie George ; Lukas Maas ; Nima Abedpour ; Maria Cartolano ; Laura Kaiser ; Rieke Nila Fischer ; Andreas H. Scheel ; Jan-Philipp Weber ; Martin Hellmich ; Graziella Bosco ; Caroline Volz ; Christian Müller ; Ilona Dahmen ; Felix John ; Cleidson Padua Alves ; Lisa Werr ; Jens Peter Panse ; Martin Kirschner ; Walburga Engel-Riedel ; Jessica Jürgens ; Erich Stoelben ; Michael Brockmann ; Stefan Grau ; Martin Sebastian ; Jan Alexander Stratmann ; Jens Kern ; Horst-Dieter Hummel ; Balazs Hegedus ; Martin Schuler ; Till Plönes ; Clemens Aigner ; Thomas Elter ; Karin Toepelt ; Yon-Dschun Ko ; Sylke Kurz ; Christian Grohé ; Monika Serke ; Katja Anne Höpker ; Lars Gerd Hagmeyer ; Fabian Doerr ; Khosro Hekmath ; Judith Strapatsas ; Karl-Otto Kambartel ; Geothy Chakupurakal ; Annette Hülsmeyer ; Franz-Georg Bauernfeind ; Frank Griesinger ; Anne Lüers ; Wiebke Dirks ; Rainer Gerhard Wiewrodt ; Andrea Luecke ; Ernst Michael Rodermann ; Andreas Diel ; Volker Hagen ; Kai Severin ; Roland Ullrich ; Christian Reinhardt ; Alexander Quaas ; Magdalena Bogus ; Cornelius Courts ; Peter Nürnberg ; Kerstin Becker ; Viktor Achter ; Reinhard Büttner ; Jürgen Wolf ; Martin Peifer ; Roman Thomas
Arbeitsgemeinschaft der Wissenschaftlichen Medizinischen Fachgesellschaften GMS german medical science Berlin : German Medical Science (GMS) gGmbH, 2003 (2021) vom: 26. Feb. Online-Ressource
von Rico Osteresch ; Andreas Fach ; Fabian-Simon Frielitz ; Sven Meyer ; Johannes Schmucker ; Stephan Rühle ; Tina Retzlaff ; Moritz Hadwiger ; Tobias Härle ; Albrecht Elsässer ; Alexander Katalinic ; Ingo Eitel ; Rainer Hambrecht ; Harm Wienbergen
