FFBDNet: Feature Fusion and Bipartite Decision Networks for Recommending Medication Combination

Abstract

Recommending medication combinations for patients is an essential part of artificial intelligence in the healthcare field. Existing approaches improve the effect of recommendations by considering how to make full use of patients’ electronic health records or by introducing additional external knowledge, but there is still room for improving the fusion of heterogeneous and diverse knowledge and the effect between accuracy and drug-drug interaction (DDI) rate. To fill this gap, we propose the Feature Fusion and Bipartite Decision Networks (FFBDNet) to leverage external knowledge and improve accuracy and DDI rate. FFBDNet is equipped with a patient feature encoder which extract useful information from current and historical visits of patient to supplement the patient’s health status, a medication feature encoder which can easily fuse the heterogeneous and diverse external knowledge of medications as feature, and a bipartite decision module to give medication recommendation results. FFBDNet also has a greedy loss function to improve accuracy and DDI rate. We demonstrate the effectiveness of FFBDNet by comparing with several state-of-the-art methods on a benchmark dataset. FFBDNet outperformed all baselines in all effective measures, reduced relatively the DDI rate by 97.65% from existing EHR data, and also is shown to improve 1.02% on Jaccard similarity.