Prescription Generation Via Multi-Source Context-Aware Hierarchical Transformer

Recent years have witnessed the widespread application of artificial intelligence technologies in major hospitals for accurate diagnoses and effective treatments. Existing methods for personalizing treatment mainly focus on recommending drug combinations for patients based on their profiles and symptoms. However, the generation of prescriptions, including drugs and the corresponding doses, based on context information during hospitalization has been largely ignored. Therefore, in this paper, we propose a multisource, context-aware prescription generation model, namely hierarchical transformer-based prescription generation (HTPG), to solve this problem. Specifically, we first formulate prescription generation as a sequence generation task, where each prescription is regarded as a sequential composition of drug-dose pairs. Then, we propose to model the multisource context information of patients and generate prescriptions based on a hierarchical transformer structure. Extensive experiments on a public real-world dataset demonstrate the effectiveness of our HTPG model compared with several competitive baseline methods.