InDISP: An Interpretable Model for Dynamic Illness Severity Prediction

Xinyu Ma, Meng Wang, Xing Liu, Yifan Yang, Yefeng Zheng, Sen Wang

Abstract: At present, there are a large number of methods for predicting the dynamic illness severity. However, these methods have two limitations: (1) they are not sensitive enough to abrupt changes in illness severity; (2) they are not comprehensive enough to explain the predicted results. To tackle these two challenges, we propose a novel Interpretable model for Dynamic Illness Severity Prediction (InDISP), effectively combining the patient status, structured medical knowledge, and drug usage to predict the trend of Sequential Organ Failure Assessment (SOFA) scores using a temporal convolutional network. The capture of drug usage events makes InDISP get better results with an abrupt change of the SOFA score. In addition, InDISP can explain the influence of features on the prediction results and explore the internal mechanism of their combined influence.