EASP

An Explainable Artificial Intelligence Predictor for Early Detection of Sepsis. The highest entry score from SailOcean in the PhysioNet/Computing in Cardiology Challenge 2019.

Brief Introduction

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.

Data

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

Training

To train a model use the following command:

python model_train.py

Note that the model is saved in directory of 'xgb_model'

Evaluation

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

Explanation

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

Citation and Reference

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.

Early Prediction of Sepsis Using Multi-Feature Fusion Based XGBoost Learning and Bayesian Optimization

Feadback

If you have any questions or suggestions on this work, please e-mail meicheng@seu.edu.cn

Meicheng-SEU/EASP