Predict and Interpret Health Risk using EHR through Typical Patients
Zhihao Yu Chaohe Zhang Yasha Wang Wen Tang Jiangtao Wang Liantao Ma
Predicting health risks from electronic health records (EHR) is a topic of
recent interest. Deep learning models have achieved success by modeling
temporal and feature interaction. However, these methods learn insufficient
representations and lead to poor performance when it comes to patients with few
visits or sparse records. Inspired by the fact that doctors may compare the
patient with typical patients and make decisions from similar cases, we propose
a Progressive Prototypical Network (PPN) to select typical patients as
prototypes and utilize their information to enhance the representation of the
given patient. In particular, a progressive prototype memory and two prototype
separation losses are proposed to update prototypes. Besides, a novel
integration is introduced for better fusing information from patients and
prototypes. Experiments on three real-world datasets demonstrate that our model
brings improvement on all metrics. To make our results better understood by
physicians, we developed an application at http://ppn.ai-care.top. Our code is
released at https://github.com/yzhHoward/PPN.