Medication Recommendation Based on a Knowledge-enhanced Pre-training Model

Abstract

More and more attention has been paid to electronic medical record (EMR)-based auxiliary diagnosis and treatment, in which medication recommendation is an important research direction. The existing medication recommendation models mainly depend on the data of patients, diagnosis and medications. However, the insufficient amount of clinical data with temporal dependencies becomes a major obstacle. This paper proposes a new knowledge-enhanced pre-training model for medication recommendation. On the one hand, the classification knowledge in diagnostic codes and drug codes is encoded by Graph Attention Network and fused into the clinical data for expanding the data content. On the other hand, a large number of single visit data of EMR are used to create the pre-trained visit model by a modified BERT for expanding the data scale. The experimental results on EMR data from more than 2,000 medical and health institutions in Hainan, China show that the fusion of classification knowledge and pre-training model can effectively improve the accuracy of medication recommendation.