Predicting Prescriptions via DSCA-Dual Sequences with Cross Attention Network.

Mining Electronic Health Records (EHRs) is of great significance to improve the efficiency and quality of medical services. In recent years, researchers have used deep recurrent neural networks to predict patients’ next-period prescriptions. The main challenges of predicting next-period prescriptions are as follows: i) The latent interdependence information which can be utilized to enhance the representation of the data between heterogeneous features is dynamic. However, most existing approaches do not consider capturing and utilizing this information. ii) Conventional recurrent neural networks cannot store historical interdependent information between heterogeneous features and take advantage of it. To address these challenges, We propose a novel attention mechanism named Cross Attention (CA) that can capture the interdependence between two sequences. We further propose three types of recurrent neural networks that can capture and utilize current and historical latent interdependence between the two sequences. Extensive experiments on real-world data demonstrate that DSCA networks can capture the interdependence information between sequences and outperform state-of-the-art methods.