SEVA Medical Sections Extraction

The repository for the SEVA PhysionNet publication "Semi-supervised Extraction, Validation and model-based Analysis of Medical Sections in MIMIC-III Patient Notes"

Repo Organization

├── LICENSE
├── data               <- The main data directory. Please store all data files from the PhysioNet project here to make the other scripts work.
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├── notebooks          <- Jupyter notebooks for loading the SVC models and generating sections from MIMIC
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├── src                <- The Python module for the sectioning code. Imported by the other notebooks and scripts.
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├── requirements.txt   <- The requirements file for the notebooks and scripts. Jupyter should already be present on your system and is thus not included here. Generated with `pip freeze > requirements.txt`
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├── setup.py           <- Makes sectioning module pip installable (pip install -e .) so src can be imported
└── src                <- Source code for use in this project.
    ├── __init__.py    <- Makes src a Python module
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    ├── data           <- Helper to load and process the trigger file etc.
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    ├── db             <- Helpers to interface Google BigQuery MIMIC tables
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    ├── models         <- Helpers to utilize the pre-trained SVC sectioning models
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    └── sectioning     <- Core module which contains all the sectioning logic

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Project based on the cookiecutter data science project template. #cookiecutterdatascience