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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 ex- traction, 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 rela- tionships into the learning weights for modeling the dependencies between and within each visit. This enables us to obtain the contri- bution 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 in- terpretable. 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 ob- jective 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 the contribution matrix and the captured uncertainty are used to assist physicians in making robust decisions. 

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 ex- traction, 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 rela- tionships into the learning weights for modeling the dependencies between and within each visit. This enables us to obtain the contri- bution 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 in- terpretable. 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 ob- jective 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 the contribution matrix and the captured uncertainty are used to assist physicians in making robust decisions.