Medical concept integrated residual short‐long temporal convolutional networks for predicting clinical events

Abstract

Large collections of electronic clinical data today provide us with a vast source of information on medical practice. However, the utilization of those data for exploratory analysis to support clinical decisions is still limited. It is particularly challenging for extracting useful disease progression patterns from such data because it is longitudinal, incomplete, irregular, and heterogeneous of the patient conditions. In this article, we propose an integrated clinical event prediction model medical concept integrated residual short-long temporal convolutional networks (SL-TCN) to address these challenges. Compared to existing models, our model has three-fold advantages: (1) it learns a compact set of medical concepts as the bridge between the hidden progression process and the observed medical evidence. ( 2) it learns a continuous-time progression model from discrete-time observations with nonequal intervals and high-dimensional features. (3) We fuse the temporal convolutional network, the long short-term memory network, and the residual connector so as to capture the local and global dependency of the sequence and make the clinical event predictions more robust. Through extensive experiments on the MIMIC III dataset, we demonstrate that our SL-TCN achieves higher precision in clinical event prediction and derives some interesting clinical insights.