INPREM: An Interpretable and Trustworthy Predictive Model for Healthcare -
INPREM: An Interpretable and Trustworthy Predictive Model for Healthcare
Aug 13, 202010 views
Xianli Zhang
Building a predictive model based on historical Electronic Health,Records (EHRs) for personalized healthcare has become an active,research area. Benefiting from the powerful ability of feature extraction, deep learning (DL) approaches have achieved promising,performance in many clinical prediction tasks. However, due to the,lack of interpretability and trustworthiness, it is difficult to apply,DL in real clinical cases of decision making. To address this, in,this paper, we propose an interpretable and trustworthy predictive,model (INPREM) for healthcare. Firstly, INPREM is designed as a,linear model for interpretability while encoding non-linear relationships into the learning weights for modeling the dependencies,between and within each visit. This enables us to obtain the contribution matrix of the input variables, which is served as the evidence,of the prediction result(s), and help physicians understand why the,model gives such a prediction, thereby making the model more interpretable. Secondly, for trustworthiness, we place a random gate,(which follows a Bernoulli distribution to turn on or off ) over each,weight of the model, as well as an additional branch to estimate,data noises. With the help of the Monto Carlo sampling and an objective function accounting for data noises, the model can capture,the uncertainty of each prediction. The captured uncertainty, in,turn, allows physicians to know how confident the model is, thus,making the model more trustworthy. We empirically demonstrate,that the proposed INPREM outperforms existing approaches with,a significant margin. A case study is also presented to show how