CDR-Detector: a chronic disease risk prediction model combining pre-training with deep reinforcement learning

Abstract

Abstract Chronic disease risk prediction based on electronic health record (EHR) is an important research direction of Internet healthcare. Current studies mainly focused on developing well-designed deep learning models to predict the disease risk based on large-scale and high-quality longitudinal EHR data. However, in real-world scenarios, people’s medical habits and low prevalence of diseases often lead to few-shot and imbalanced longitudinal EHR data. This has become an urgent challenge for chronic disease risk prediction based on EHR. Aiming at this challenge, this study combines EHR based pre-training and deep reinforcement learning to develop a novel chronic disease risk prediction model called CDR-Detector. The model adopts the Q-learning architecture with a custom reward function. In order to improve the few-shot learning ability of model, a self-adaptive EHR based pre-training model with two new pre-training tasks is developed to mine valuable dependencies from single-visit EHR data. In order to solve the problem of data imbalance, a dual experience replay strategy is realized to help the model select representative data samples and accelerate model convergence on the imbalanced EHR data. A group of experiments have been conducted on real personal physical examination data. Experimental results show that, compared with the existing state-of-art methods, the proposed CDR-Detector has better accuracy and robustness on the few-shot and imbalanced EHR data.

Keywords

• Chronic risk prediction
• Data imbalance
• Electronic health records
• Few-shot learning
• Reinforcement learning