Dual-Channel Semantic-Enhanced Combinatorial Medication Recommendation via Knowledge Distillation

Abstract

As a vital task in healthcare, combinatorial medication recommendation aims to generate drug combinations tailored to patient health status. Precisely capturing the rich semantic information within clinical narratives is crucial for achieving this goal. However, existing approaches primarily rely on isolated identifiers (i.e. patient IDs, drug codes), failing to leverage the inherent semantic associations between patient conditions and medication descriptions. To fill this gap, we propose the Dual-Channel Semantics-Enhanced Network (DCSENet), a novel dual-channel framework that explicitly incorporates context-rich clinical narratives knowledge. DCSENet fine-tunes domain-adapted pre-trained language models (LMs) to capture semantic correlations between patient status and medication narratives. A transformer-based dual-channel decoder decodes the semantic information at the disease-level and the patient-level respectively. The disease-level channel focuses on the natural text semantic associations between diseases and drugs, while the patient-channel provides personalized features. To mitigate the prohibitive computational overhead of the LMs in clinical deployment, we introduce an attention-map-based knowledge distillation mechanism that efficiently transfers semantic knowledge from the LMs into an identifier-based (ID-based) target model. Extensive experiments on MIMIC-Ⅲ and MIMIC-Ⅳ datasets demonstrate that DCSENet outperforms existing state-of-the-art methods in recommendation accuracy while maintaining a low computational cost.