DAJLENet: A neural network based on dual attention and joint learning for explainable heart failure adverse event prediction

Tianhan Xu, Jinxiang Zhang, Bin Li

Abstract: Heart Failure (HF) is a common, high-risk, complex cardiovascular disease that is often accompanied by other diseases and has a high mortality rate. The use of electronic health records (EHRs) to predict the risk of adverse events, such as death, intensive care units (ICU) transfer, and readmission, in patients with HF is an important topic in the field of personalized treatment and prognosis. However, the heterogeneity and diversity of patient EHRs make it difficult for traditional machine learning models to adequately mine the hidden features and well train patient representations. In addition, significant sample imbalance and lack of labeled data can limit model performance. To address the above issues, this study proposes a neural network called DAJLENet for predicting adverse events in heart failure with explainability. The model obtains feature-level and visit-level patient representations through feature mining, feature embedding, and dual attention mechanisms. Based on the patient representation, a joint training method of classification task and contrastive learning task is used to learn shared representations to alleviate the sample imbalance problem and improve the model performance. Experimental results on four heart failure prediction tasks show that DAJLENet has an improvement of 1%–3% on various metrics compared to the most advanced models, and an improvement of more than 5% on unbalanced datasets.

External IDs:doi:10.1016/j.compeleceng.2024.109359