An Explainable Artificial Intelligence Predictor for Early Detection of Sepsis. The highest entry score from SailOcean in the PhysioNet/Computing in Cardiology Challenge 2019.
The PhysioNet/Computing in Cardiology Challenge 2019 facilitated the development of automated, open-source algorithms for the early detection of sepsis from clinical data. Details see (https://physionet.org/content/challenge-2019/1.0.0/).
We proposed an Explainable Artificial-intelligence Sepsis Predictor (EASP) to predict sepsis risk hour-by-hour, and focused on its interpretability for the clinical EHR data sourced from ICU patients. Final results show that EASP achieved best performance in the challenge.
These instructions go through the training and evaluation of our model on the Physionet 2019 challenge public database (https://archive.physionet.org/users/shared/challenge-2019/).
To download and build the datasets run:
./setup.sh
To train a model use the following command:
python model_train.py
Note that the model is saved in directory of 'xgb_model'
After training the model, you can make predictions and then yield the model performance.
python test.py xgb_model
Or you can directly use our trained model for quick verification using the following command.
python test.py Submit_model
Impacts of features on risk output were quantified by Shapley values to obtain instant interpretability for the developed EASP model.
python shap_explain.py xgb_model
or
python shap_explain.py Submit_model
This work has been published in Critical Care Medicine.
An Explainable Artificial Intelligence Predictor for Early Detection of Sepsis
Conference Paper published in 2019 Computing in Cardiology Conference is as follows.
If you have any questions or suggestions on this work, please e-mail [email protected]