AKA-SafeMed: A safe medication recommendation based on attention mechanism and knowledge augmentation

Abstract

Medication recommendation (MR) focuses on generating a medication combination without adverse drug-drug interactions (DDIs) based on electronic health records (EHRs) in making prescriptions. However, how to capture the temporality in historical visits and concurrence in clinical events are two challenges that determine the outcomes of an MR model. To achieve effective and safe MR with imbalanced EHR data, we propose a medication recommendation model based on the attention mechanism and knowledge augmentation strategy (AKA-SafeMed). Specifically, in the patient representation module, bidirectional long short-term memory (BiLSTM) models are deployed to encode the patient's sequential visits, and a self-attention mechanism is employed to learn the weights of diagnosis and procedure vectors from a patient's historical visits; in the medication generation module, the EHR and DDI graphs are leveraged to fully explore the relationships between pairwise drugs as a knowledge augmentation strategy. Finally, extensive experiments are conducted to evaluate the performance of AKA-SafeMed on public EHR datasets. The experimental results demonstrate that the AKA-SafeMed model achieves superior performance in MR tasks compared with the state-of-the-art baseline models.