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Version](https://img.shields.io/pypi/pyversions/ehrapy)](https://pypi.org/project/ehrapy)\n[![Read the Docs](https://img.shields.io/readthedocs/ehrapy/latest.svg?label=Read%20the%20Docs)](https://ehrapy.readthedocs.io/)\n[![Test](https://github.com/theislab/ehrapy/actions/workflows/test.yml/badge.svg)](https://github.com/theislab/ehrapy/actions/workflows/test.yml)\n[![pre-commit](https://img.shields.io/badge/pre--commit-enabled-brightgreen?logo=pre-commit\u0026logoColor=white)](https://github.com/pre-commit/pre-commit)\n\n\u003cp align=\"center\"\u003e\n  \u003cimg src=\"https://user-images.githubusercontent.com/21954664/156930990-0d668468-0cd9-496e-995a-96d2c2407cf5.png\" alt=\"ehrapy logo\" width=\"25%\"\u003e\n\u003c/p\u003e\n\n\n# ehrapy overview\n\n**ehrapy** is a modular open-source Python framework for exploratory analysis of heterogeneous epidemiological and EHR data.\nIt supports a full pipeline from data ingestion and quality control to advanced analyses such as clustering, survival, trajectory, causal inference, deep learning, and more.\n\n\u003cp align=\"center\"\u003e\n  \u003cimg src=\"https://github.com/user-attachments/assets/bf20359d-363f-4ee9-a71c-82310f57a3ad\" alt=\"fig1\" width=\"100%\"\u003e\n\u003c/p\u003e\n\n## Documentation\n\nPlease read the [documentation](https://ehrapy.readthedocs.io/en/latest) for installation, tutorials, use cases, and more.\n\n## Installation\n\nYou can install _ehrapy_ via [pip] from [PyPI]:\n\n```console\n$ pip install ehrapy\n```\n\n## API\n\nPlease have a look at the [API documentation](https://ehrapy.readthedocs.io/en/latest/api.html) and the [tutorials](https://ehrapy.readthedocs.io/en/latest/tutorials/index.html).\n\n```python\nimport ehrapy as ep\n```\n\n## Citation\n\n \u003cp align=\"center\"\u003e\n  \u003ca href=\"https://www.nature.com/articles/s41591-024-03214-0\"\u003e\n    \u003cimg src=\"https://github.com/user-attachments/assets/c3f7e79d-1633-4767-9dda-e94262279685\" alt=\"fig2\" width=\"50%\"\u003e\n  \u003c/a\u003e\n\u003c/p\u003e\n\nRead more about ehrapy in the [associated publication](https://doi.org/10.1038/s41591-024-03214-0).\n\n```bibtex\n@article{Heumos2024,\n  author = {Heumos, Lukas and Ehmele, Philipp and Treis, Tim and Upmeier zu Belzen, Julius and Roellin, Eljas and May, Lilly and Namsaraeva, Altana and Horlava, Nastassya and Shitov, Vladimir A. and Zhang, Xinyue and Zappia, Luke and Knoll, Rainer and Lang, Niklas J. and Hetzel, Leon and Virshup, Isaac and Sikkema, Lisa and Curion, Fabiola and Eils, Roland and Schiller, Herbert B. and Hilgendorff, Anne and Theis, Fabian J.},\n  year = {2024},\n  month = {11},\n  day = {01},\n  title = {An open-source framework for end-to-end analysis of electronic health record data},\n  journal = {Nature Medicine},\n  volume = {30},\n  number = {11},\n  pages = {3369--3380},\n  issn = {1546-170X},\n  doi = {10.1038/s41591-024-03214-0},\n  url = {https://doi.org/10.1038/s41591-024-03214-0}\n}\n```\n\n[pip]: https://pip.pypa.io/\n[pypi]: https://pypi.org/\n[api]: https://ehrapy.readthedocs.io/en/latest/api.html\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ftheislab%2Fehrapy","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Ftheislab%2Fehrapy","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ftheislab%2Fehrapy/lists"}