A Multi-Task Neural Network Architecture for Renal Dysfunction Prediction in Heart Failure Patients with Electronic Health Records

Abstract

Renal dysfunction, which is associated with bad clinical outcomes, is one of the most common complications of heart failure (HF). Timely prediction of renal dysfunction can help medical staffs intervene early to avoid catastrophic consequences. In this paper, we proposed a multi-task deep and wide neural network (MT-DWNN) for predicting fatal complications during hospitalization. The algorithm was tested on a dataset collected from Chinese PLA General Hospital, which contains 35,101 hospitalizations with HF diagnosis during the last 18 years, and 2,478 hospitalizations with a diagnosis of renal dysfunction. For the renal dysfunction task, the AUC of the proposed method is 0.9393, which is a significant improvement ( $p < 0.01$ ) compared to that of conventional methods, while that of single task deep neural networks is 0.9370, that of random forest is 0.9360, and that of logistic regression is 0.9233. The experimental results show that the proposed MT-DWNN model achieves better prediction performance on renal dysfunction in HF patients than conventional models.