Deep Cardiovascular Disease Prediction with Risk Factors Powered Bi-attention

Abstract

Background Cardiovascular disease (CVD), as a chronic disease, has been perplexing human beings and is one of the serious diseases endangering life and health. Therefore, using the electronic medical record information of patients to automatically predict CVD has important application value in intelligent auxiliary diagnosis and treatment, and is a hot issue in intelligent medical research. In recent years, attention mechanism has been successfully extended to various tasks of natural language processing. Typically, these methods use attention to focus on a small part of the context and summarize it with a fixed-size vector, coupling attention in time, and/or often forming a uni-directional attention. Methods In this paper, we propose a CVD risk factors powered bi-directional attention (RFPBiA) network, which is a multi-stage hierarchical process, representing information fusion at different granularity levels, and uses the bi-directional attention to obtain the text representation of risk factors without early aggregation. Results The experimental results show that the proposed method can obviously improve the performance of CVD prediction, and the F-score reaches 0.9424, which is better than the existing related methods. Conclusions We propose to extract the risk factors leading to CVD by using the existing mature entity recognition technology, which provides a new idea for disease prediction tasks. Moreover, the memory-less attention mechanism in both directions in our proposed prediction model of RFPBiA can fuse the character sequence and the risk factors contained in the electronic medical record text to predict CVD.