Pretraining-Based Relevance-Aware Visit Similarity Network for Drug Recommendation

Drug recommendation based on electronic health record (EHR) is fundamental to effective disease treatment. Similar to commercial sequence-based recommendation systems, the accuracy of drug recommendation largely depends on precise patient modeling. However, patient modeling is more complex, as it not only requires sequence modeling of patient's disease course, but also needs to refer to the information of patients with similar medical medication. In EHR data, many patients have only one visit record, and the similarity between patients is often vague and unclear, which may cause noise and ambiguity. This leads to significant challenges for the drug recommendation field, especially when patient records are sparse or when patient similarity is vague. To address the above challenges, we propose RaVSNet (Relevance aware Visit Similarity Network), which improves drug recommendation by leveraging both longitudinal and transversal visit similarity and integrating medical relevance knowledge. RaVSNet utilizes multi-dimensional visit information similar to the patient's current visit as a reference, and employs a relevance-aware network to explicitly model the matching relationships between medical conditions and medications. Additionally, RaVSNet designs a general pretraining framework specifically for drug recommendation, including two tasks, Medication Sequence Reconstruction (MSR) and Causal Effect Inference (CEI), to discover the deep connections between medical information and medications. Experimental results on two public EHR datasets, MIMIC-III and MIMIC-IV demonstrate that the proposed algorithm outperforms state-of-the-art methods, yielding more accurate drug recommendation combinations, and the proposed general pretraining framework can be seamlessly integrated into most drug recommendation methods to achieve performance improvements.