GitHub associated with the research paper "Data-based Pharmacodynamic Modeling for BIS and Mean Arterial Pressure Prediction during General Anesthesia" accepted for ECC23.
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├─── lee_code # folder to reproduce the method of lee et al.
├─── code_lee_v2.py # code from Lee et al. modified for tensorflow v2
├─── create_dataset_lee.py # create the dataset that can be used by code_lee_v2.py
├─── test_lee.py # ??
├─── data # folder to store the data used in the code
├─── info_clinic_vitalDB.csv # personnal information of the patient in the vitalDB dataset
├─── create_dataset.py # download the needed data from vitaldb dataset
├─── separate_train_test.py # separate test and train set using the change of target as the critrion
├─── select_cases.py # filter the case from vitalDB to obtain the case_id of the patient used in the study
├─── all_reg.py # test different regressor for predicting BIS and MAP along the surgery
├─── delayed_pred.py # test the SVR regressor to predict BIS and MAP in the future for different time ahead
├─── svr_kernel.py # test different kernel for the SVR regressor
├─── standard_model.py # test the standard model for predicting BIS and MAP
├─── metrics_function.py # include function to compute prediction metrics and create usefull plot
├── LICENSE
├── requirements.txt
├── README.md
└── .gitignore
Install all the required packages with the command:
pip install -r requirement.txt
For standard model and regression evaluation:
- First the database must be created from online vitalDB data. for this run the code Create_dataset.py
- Then Standard models and Regressions technique can be evaluated by launching the corresponding scripts, hyperparameters are at the beginning of the script.
For Lee et al. model evaluation open lee_code folder:
- First create the dedicated database (with only test cases) launching Create_dataset_lee.py
- Launch the script test_lee.py to evaluate their method on our selected cases
Bob Aubouin--Pairault, Mirko Fiacchini, Thao Dang
MIT license