Time-Aware Multi-Type Data Fusion Representation Learning Framework for Risk Prediction of Cardiovascular Diseases

Abstract

Predicting the future risk of cardiovascular diseases from the historical Electronic Health Records (EHRs) is a significant research task in personalized healthcare fields. In recent years, many deep neural network-based methods have emerged, which model patient disease progression by capturing the temporal patterns in sequential visit data. However, most existing methods that integrate different types of clinical data often do not fully consider the impact of the patient's age and the irregular time interval between consecutive medical records on the patient's disease development. To address these challenges, we propose a Time-Aware Multi-type Data fUsion Representation learning framework (TAMDUR) for cardiovascular diseases (CVDs) risk prediction. In this framework, we design a time-aware decay function, which is based on the patient's age and the elapsed time between visits, to model the disease progression pattern. Then, a parallel combination of bidirectional long short-term memory (Bi_LSTM) network and convolutional neural network (CNN) is constructed to respectively learn the temporal and non-temporal features from various types of clinical data. Finally, a multi-type data fusion representation layer based on self-attention is utilized to integrate various features and their correlations to obtain the final patient representation. We evaluate our model on a real medical dataset, and the experimental results demonstrate that TAMDUR outperforms the state-of-the-art approaches.