We developed ehrapy, an open-source Python software framework for the exploratory analysis of electronic health record data. Ehrapy handles various widely used data formats, preprocessing tasks such as imputation of missing data and bias detection, and offers tools for analyses including patient stratification, survival analysis, causal inference and trajectory inference.
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References
Cios, K. J. & Moore, G. W. Uniqueness of medical data mining. Artif. Intell. Med. 26, 1–24 (2002). A review article that presents the challenges and opportunities of medical data.
Virshup, I. et al. The scverse project provides a computational ecosystem for single-cell omics data analysis. Nat. Biotechnol. 41, 604–606 (2023). This paper reports the scverse open-source consortium that our solution builds upon.
Moor, M. et al. Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023). A review article that presents the potential of foundation models in medicine.
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This is a summary of: Heumos, L. et al. An open-source framework for end-to-end analysis of electronic health record data. Nat. Med. https://doi.org/10.1038/s41591-024-03214-0 (2024).
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An open-source framework for electronic health record and patient fate exploration. Nat Med (2024). https://doi.org/10.1038/s41591-024-03298-8
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DOI: https://doi.org/10.1038/s41591-024-03298-8
