Hypotension_pred

Use a data-based approach to predict intra-operative hypotension.

Installation

Use a new virtual env and Python 3.11 (with pyenv) for maximal compatibility.

git clone https://github.com/BobAubouin/hypotension_pred hp_pred
cd hp_pred
pip install .

Dev / Contribution

In addition, you can add the optional build dev. So you will download the Python packages required to develop the project (unit test, linter, formatter).

git clone https://github.com/BobAubouin/hypotension_pred hp_pred
cd hp_pred
pip install -e .[dev]

Use

Download raw data from VitalDB

The data used are from the VitalDB open dataset. You must read the Data Use Agreement before using it.

To download the data you can use the package's command python -m hp_pred.dataset_download. The help command outputs the following:

usage: dataset_download.py [-h] [-l {CRITICAL,FATAL,ERROR,WARN,WARNING,INFO,DEBUG,NOTSET}] [-s GROUP_SIZE] [-o OUTPUT_FOLDER]

Download the VitalDB data for hypertension prediction.

options:
  -h, --help            show this help message and exit
  -l {CRITICAL,FATAL,ERROR,WARN,WARNING,INFO,DEBUG,NOTSET}, --log_level_name {CRITICAL,FATAL,ERROR,WARN,WARNING,INFO,DEBUG,NOTSET}
                        The logger level name to generate logs. (default: INFO)
  -s GROUP_SIZE, --group_size GROUP_SIZE
                        Amount of cases dowloaded and processed. (default: 950)
  -o OUTPUT_FOLDER, --output_folder OUTPUT_FOLDER
                        The folder to store the data and logs. (default: data)

Create the segmented dataset

The class hp_pred.databuilder.DataBuilder is used to create the segmented dataset with a sliding window approach. An example of use is given in the scripts/dataset_build/base_dataset.py scripts.

Recreate CDC results

The results associated with our paper can be replicated using the version of the git tagged 'cdc_XP'. You should create the database running the previously mentioned step. Then you should run the notebooks in the scripts/experiments folder. This exact order of running must be respected:

• baseline.ipynb
• xgboos-model.ipynb
• study_leading_time.ipynb

Citation

If you use this code in your research, please cite our paper.