HiRef: Leveraging Hierarchical Ontology and Network Refinement for Robust Medication Recommendation

• URL: http://arxiv.org/abs/2508.10425v1
• Date: Thu, 14 Aug 2025 07:55:03 GMT
• Title: HiRef: Leveraging Hierarchical Ontology and Network Refinement for Robust Medication Recommendation
• Authors: Yan Ting Chok, Soyon Park, Seungheun Baek, Hajung Kim, Junhyun Lee, Jaewoo Kang,
• Abstract summary: We propose Hierarchical Ontology and Network Refinement for Robust Medication Recommendation (HiRef)<n>We embed entities in hyperbolic space, which naturally captures tree-like relationships and enables knowledge transfer through shared ancestors.<n>Our model achieves strong performance on EHR benchmarks (MIMIC-III and MIMIC-IV) and maintains high accuracy under simulated unseen-code settings.
• Score: 17.45722229030237
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Medication recommendation is a crucial task for assisting physicians in making timely decisions from longitudinal patient medical records. However, real-world EHR data present significant challenges due to the presence of rarely observed medical entities and incomplete records that may not fully capture the clinical ground truth. While data-driven models trained on longitudinal Electronic Health Records often achieve strong empirical performance, they struggle to generalize under missing or novel conditions, largely due to their reliance on observed co-occurrence patterns. To address these issues, we propose Hierarchical Ontology and Network Refinement for Robust Medication Recommendation (HiRef), a unified framework that combines two complementary structures: (i) the hierarchical semantics encoded in curated medical ontologies, and (ii) refined co-occurrence patterns derived from real-world EHRs. We embed ontology entities in hyperbolic space, which naturally captures tree-like relationships and enables knowledge transfer through shared ancestors, thereby improving generalizability to unseen codes. To further improve robustness, we introduce a prior-guided sparse regularization scheme that refines the EHR co-occurrence graph by suppressing spurious edges while preserving clinically meaningful associations. Our model achieves strong performance on EHR benchmarks (MIMIC-III and MIMIC-IV) and maintains high accuracy under simulated unseen-code settings. Extensive experiments with comprehensive ablation studies demonstrate HiRef's resilience to unseen medical codes, supported by in-depth analyses of the learned sparsified graph structure and medical code embeddings.

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