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--- |
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title: ClinFly |
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emoji: ✈️ |
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sdk_version: 1.25.0 |
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sdk: streamlit |
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app_file: clinfly_app_st.py |
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pinned: true |
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--- |
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# ClinFly |
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Contact : [kevin.yauy@chu-montpellier.fr](mailto:kevin.yauy@chu-montpellier.fr) |
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## Introduction |
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ClinFly is an automated framework designed to facilitate precision medicine (PM) for rare diseases. It addresses the challenge of precision phenotyping and data sharing across different languages. |
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ClinFly can anonymize, translate, and summarize clinical reports using Human Phenotype Ontology (HPO) terms, ensuring compliance with medical data privacy standards. The output includes a de-identified translated clinical report and a summary report in HPO format. |
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By streamlining the translation and anonymization of clinical reports, ClinFly aims to enhance inter-hospital data sharing, expedite medical discoveries, and pave the way for an international patient file accessible to non-English speakers. |
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## Pipeline |
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## Installation |
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To install ClinFly on your local machine, you need the `poetry` package manager. Navigate to the project folder and run: |
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``` |
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poetry install |
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``` |
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You'll need [`poppler`](https://pdf2image.readthedocs.io/en/latest/installation.html) and [`tesseract-ocr`](https://tesseract-ocr.github.io/tessdoc/Installation.html) installed in your os to use OCR on pdf file. |
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If you need to generate a `requirements.txt` file, use the following command: |
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``` |
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poetry export --without-hashes --format=requirements.txt > requirements.txt |
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``` |
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## Usage |
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### Graphical User Interface |
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For single report usage with interactive analysis, ClinFly provides a web application accessible at https://huggingface.co/spaces/kyauy/ClinFly. |
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To run the Streamlit application on your local computer, activate the poetry shell and run the `clinfly_app_st.py` file: |
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``` |
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poetry shell |
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streamlit run clinfly_app_st.py |
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``` |
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### Command Line Interface |
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For processing multiple reports with offline options, use the command line interface provided by `clinfly_app_cli.py`. |
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The input should be a TSV .tsv file structured as follows (see `data/test.tsv` for an example): |
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``` |
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Report_id_1 Doe John Report text |
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... |
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Report_id_X Doe John Report text |
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``` |
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You can also put a symlink to a pdf file (see `data/test_pdf.tsv` for an example) |
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``` |
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Report_id_X Doe John Example.pdf |
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``` |
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Outputs will be placed in the `results` folder according to the file extension, using first three columns in filename. |
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- The deidentify report will be generated and placed in the `results/Reports` folder. |
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- Three HPO extraction outputs will be generated in `TSV`, `TXT` and `JSON` folders. |
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To run the CLI application on your local computer : |
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``` |
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poetry shell |
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<python running version> clinfly_app_cli.py --file <input txt file with the reports> --language <language of the file> --model_dir <The output directory of the model (OPTIONAL)> --result_dir <The output directory of the generated result (OPTIONAL)> |
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``` |
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## Acknoledgments and reference |
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ClinFly is a tool developed by University Hospital of Montpellier and Hospices Civils de Lyon. Thanks to the developers, especially Enody Gernet for his contribution. |
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If you use ClinFly, please cite: |
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> Gauthier et al., Assessing feasibility and risk to translate, de-identify and summarize medical letters using deep learning. medrXiv (2023). [https://doi.org/10.1101/2023.07.27.23293234](https://doi.org/10.1101/2023.07.27.23293234) |
