Abstract
Accurately predicting the clinical endpoint in ICU based on the patient's electronic medical records (EMRs) is essential for the timely treatment of critically ill patients and allocation of medical resources. However, the patient's EMRs usually consist of a large amount of heterogeneous multivariate time series data such as laboratory tests and vital signs, which are produced irregularly. Most existing methods fail to effectively model the time irregularity inherent in longitudinal patient medical records and capture the interrelationships among different types of data. To tackle these limitations, we propose a novel time-aware transformer-based hierarchical attention network (TERTIAN) for clinical endpoint prediction. In this model, a time-aware transformer is introduced to learn the personalized irregular temporal patterns of medical events, and a hierarchical attention mechanism is deployed to get the accurate patient fusion representation by comprehensively mining the interactions and correlations among multiple types of medical data. We evaluate our model on the MIMIC-III dataset and MIMIC-IV dataset for the task of mortality prediction, and the results show that TERTIAN achieves higher performance than state-of-the-art approaches.