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Predicting health risks from electronic health records (EHR) is a topic ofrecent interest. Deep learning models have achieved success by modelingtemporal and feature interaction. However, these methods learn insufficientrepresentations and lead to poor performance when it comes to patients with fewvisits or sparse records. Inspired by the fact that doctors may compare thepatient with typical patients and make decisions from similar cases, we proposea Progressive Prototypical Network (PPN) to select typical patients asprototypes and utilize their information to enhance the representation of thegiven patient. In particular, a progressive prototype memory and two prototypeseparation losses are proposed to update prototypes. Besides, a novelintegration is introduced for better fusing information from patients andprototypes. Experiments on three real-world datasets demonstrate that our modelbrings improvement on all metrics. To make our results better understood byphysicians, we developed an application at http://ppn.ai-care.top. Our code isreleased at https://github.com/yzhHoward/PPN.

Predict and Interpret Health Risk using EHR through Typical Patients