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GraphDiffMed: Knowledge-Constrained Differential Attention with Pharmacological Graph Priors for Medication Recommendation

Summary

GraphDiffMed is a new framework that combines dual-scale differential attention with pharmacological graph priors to recommend medication combinations from electronic health records. By filtering noise at both visit and longitudinal levels, it achieves better recommendation quality and safety balance than existing methods.

SourcearXiv Machine LearningAuthor: Krati Saxena, Tomohiro Shibata
GraphDiffMed: Knowledge-Constrained Differential Attention with Pharmacological Graph Priors for Medication Recommendation
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[2605.20188] GraphDiffMed: Knowledge-Constrained Differential Attention with Pharmacological Graph Priors for Medication Recommendation

[Submitted on 21 Mar 2026]

Title:GraphDiffMed: Knowledge-Constrained Differential Attention with Pharmacological Graph Priors for Medication Recommendation

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Abstract:Recommending safe and effective medication combinations from electronic health records (EHRs) is a core clinical AI problem, yet it remains difficult because patient trajectories are long, noisy, and clinically heterogeneous. Existing methods typically excel at either temporal modeling across visits or pharmacological knowledge integration (e.g., drug-drug interactions, DDIs), but rarely achieve both while robustly suppressing noise. We present GraphDiffMed, a knowledge-constrained medication recommendation framework built on dual-scale Differential Attention v2. Differential attention is applied at both intra-visit and inter-visit levels to filter spurious signals within encounters and across longitudinal history, while pharmacological constraints are incorporated during learning. Experiments on MIMIC-III and ablation studies show that this design consistently improves recommendation quality and ranking over strong baselines while achieving a more favorable safety performance balance. We further find that the strongest-performing configuration uses only demographic auxiliary features under our experimental setting. Overall, GraphDiffMed demonstrates that combining noise-aware attention with pharmacological constraints yields more reliable and clinically meaningful medication recommendation. We open-source our code at this https URL.

Subjects:

Machine Learning (cs.LG); Artificial Intelligence (cs.AI)

Cite as: arXiv:2605.20188 [cs.LG]

(or arXiv:2605.20188v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2605.20188

arXiv-issued DOI via DataCite

Submission history

From: Krati Saxena [view email] [v1] Sat, 21 Mar 2026 15:10:54 UTC (1,438 KB)

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Key points and analysis

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Key points

  • GraphDiffMed uses dual-scale Differential Attention v2 to filter spurious signals within and across patient visits.
  • Pharmacological graph constraints are integrated during learning to improve safety.
  • Experiments on MIMIC-III show improved recommendation quality and safety-performance tradeoff.
  • The best configuration uses only demographic features, highlighting the efficiency of the attention mechanism.

Highlights and analysis are generated automatically and may contain errors. Check the original source.