Learning Collective Medication Effects via Multi-level Abstraction for Medication Recommendation

TL;DR

This paper introduces MSAM, a novel model that uses multi-level medication abstraction and graph reasoning to better capture the collective therapeutic effects of drug combinations, improving medication recommendations.

Contribution

MSAM is the first to incorporate multi-level semantic abstraction and graph reasoning to model collective medication effects in recommendation systems.

Findings

MSAM outperforms existing methods on real-world datasets.

Multi-level abstraction improves understanding of drug interactions.

Structural medication abstraction enhances recommendation accuracy.

Abstract

Historical prescriptions and selected candidate drugs relevant to the current visit serve as important references for medication recommendation. However, in the absence of explicit intrinsic principles for semantic composition, existing methods treat synergistic drugs as independent entities and fail to capture their collective therapeutic effects, resulting in a mismatch between medication-level references and longitudinal patient representations. In this paper, we propose MSAM, a novel medication recommendation model that bridges the gap via multi-level medication abstraction. The model introduces a multi-head graph reasoning mechanism to organize flat daily medication sets into clinically meaningful semantic units, serving as intermediate abstraction results to propagate features from individual drugs to higher-level representations. Building on these units, MSAM performs two-stage abstraction over historical prescriptions and selected candidates via intra- and inter-level feature propagation across heterogeneous clinical structures, capturing collective therapeutic effects aligned with patient conditions. Experiments on two real-world clinical datasets show that MSAM consistently outperforms state-of-the-art methods, validating the effectiveness of structural medication abstraction for recommendation.