HypeMed: Enhancing Medication Recommendations with Hypergraph-Based Patient Relationships

• URL: http://arxiv.org/abs/2603.18459v1
• Date: Thu, 19 Mar 2026 03:40:02 GMT
• Title: HypeMed: Enhancing Medication Recommendations with Hypergraph-Based Patient Relationships
• Abstract summary: HypeMed is a two-stage hypergraph-based framework unifying intra-visit coherence modeling and inter-visit augmentation.<n>In the first stage, MedRep encodes clinical visits as hyperedges via knowledge-aware contrastive pre-training, creating a globally consistent, retrieval-friendly embedding space.<n>In the second stage, SimMR performs dynamic retrieval within this space, fusing retrieved references with the patient's longitudinal data to refine medication prediction.
• Score: 42.256653836522936
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Medication recommendations aim to generate safe and effective medication sets from health records. However, accurately recommending medications hinges on inferring a patient's latent clinical condition from sparse and noisy observations, which requires both (i) preserving the visit-level combinatorial semantics of co-occurring entities and (ii) leveraging informative historical references through effective, visit-conditioned retrieval. Most existing methods fall short in one of both aspects: graph-based modeling often fragments higher-order intra-visit patterns into pairwise relations, while inter-visit augmentation methods commonly exhibit an imbalance between learning a globally stable representation space and performing dynamic retrieval within it. To address these limitations, this paper proposes HypeMed, a two-stage hypergraph-based framework unifying intra-visit coherence modeling and inter-visit augmentation. HypeMed consists of two core modules: MedRep for representation pre-training, and SimMR for similarity-enhanced recommendation. In the first stage, MedRep encodes clinical visits as hyperedges via knowledge-aware contrastive pre-training, creating a globally consistent, retrieval-friendly embedding space. In the second stage, SimMR performs dynamic retrieval within this space, fusing retrieved references with the patient's longitudinal data to refine medication prediction. Evaluation on real-world benchmarks shows that HypeMed outperforms state-of-the-art baselines in both recommendation precision and DDI reduction, simultaneously enhancing the effectiveness and safety of clinical decision support.

Related papers

• GraphDiffMed: Knowledge-Constrained Differential Attention with Pharmacological Graph Priors for Medication Recommendation [0.0828720658988688]
  We present GraphDiffMed, a knowledge-constrained medication recommendation framework built on dual-scale Differential Attention v2.<n> Experiments on MIMIC-III and ablation studies show that this design consistently improves recommendation quality and ranking over strong baselines.
  arXiv  Detail & Related papers  (2026-03-21T15:10:54Z)
• Towards Reliable Medical LLMs: Benchmarking and Enhancing Confidence Estimation of Large Language Models in Medical Consultation [97.36081721024728]
  We propose the first benchmark for assessing confidence in multi-turn interaction during realistic medical consultations.<n>Our benchmark unifies three types of medical data for open-ended diagnostic generation.<n>We present MedConf, an evidence-grounded linguistic self-assessment framework.
  arXiv  Detail & Related papers  (2026-01-22T04:51:39Z)
• MedAlign: A Synergistic Framework of Multimodal Preference Optimization and Federated Meta-Cognitive Reasoning [52.064286116035134]
  We develop MedAlign, a framework to ensure visually accurate LVLM responses for Medical Visual Question Answering (Med-VQA)<n>We first propose a multimodal Direct Preference Optimization (mDPO) objective to align preference learning with visual context.<n>We then design a Retrieval-Aware Mixture-of-Experts (RA-MoE) architecture that utilizes image and text similarity to route queries to a specialized and context-augmented LVLM.
  arXiv  Detail & Related papers  (2025-10-24T02:11:05Z)
• HiRef: Leveraging Hierarchical Ontology and Network Refinement for Robust Medication Recommendation [17.45722229030237]
  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.
  arXiv  Detail & Related papers  (2025-08-14T07:55:03Z)
• Multimodal Forecasting of Sparse Intraoperative Hypotension Events Powered by Language Model [14.69824092898171]
  Intraoperative hypotension (IOH) frequently occurs under general anesthesia and is strongly linked to adverse outcomes such as myocardial injury and increased mortality.<n>Despite its significance, IOH prediction is hindered by event sparsity and the challenge of integrating static and dynamic data across diverse patients.<n>We propose textbfIOHFuseLM, a multimodal language model framework to accurately identify and differentiate sparse hypotensive events.
  arXiv  Detail & Related papers  (2025-05-28T08:44:55Z)
• DNMDR: Dynamic Networks and Multi-view Drug Representations for Safe Medication Recommendation [9.504676133698895]
  This paper proposes a novel Medication Recommendation (MR) method with the integration of dynamic networks and multi-view drug representations (DNMDR)<n>The proposed DNMDR method outperforms the state-of-the-art baseline models with a large margin on various metrics such as PRAUC, Jaccard, DDI rates and so on.
  arXiv  Detail & Related papers  (2025-01-15T04:36:55Z)
• Contrastive Learning on Medical Intents for Sequential Prescription Recommendation [7.780844394603662]
  Attentive Recommendation with Contrasted Intents (ARCI) is designed to capture the different but coexisting temporal paths across a shared sequence of visits. We conducted experiments on two real-world datasets for the prescription recommendation task using both ranking and classification metrics.
  arXiv  Detail & Related papers  (2024-08-13T20:10:28Z)
• StratMed: Relevance Stratification between Biomedical Entities for Sparsity on Medication Recommendation [9.296433860766165]
  StratMed is a stratification strategy that overcomes the long-tailed problem and achieves fuller learning of sparse data. It also utilizes a dual-property network to address the issue of mutual constraints on the safety and accuracy of medication combinations. Our model reduces safety risk by 15.08%, improves accuracy by 0.36%, and reduces training time consumption by 81.66%.
  arXiv  Detail & Related papers  (2023-08-31T14:59:32Z)
• Real-time landmark detection for precise endoscopic submucosal dissection via shape-aware relation network [51.44506007844284]
  We propose a shape-aware relation network for accurate and real-time landmark detection in endoscopic submucosal dissection surgery. We first devise an algorithm to automatically generate relation keypoint heatmaps, which intuitively represent the prior knowledge of spatial relations among landmarks. We then develop two complementary regularization schemes to progressively incorporate the prior knowledge into the training process.
  arXiv  Detail & Related papers  (2021-11-08T07:57:30Z)
• MIMO: Mutual Integration of Patient Journey and Medical Ontology for Healthcare Representation Learning [49.57261599776167]
  We propose an end-to-end robust Transformer-based solution, Mutual Integration of patient journey and Medical Ontology (MIMO) for healthcare representation learning and predictive analytics.
  arXiv  Detail & Related papers  (2021-07-20T07:04:52Z)
• BiteNet: Bidirectional Temporal Encoder Network to Predict Medical Outcomes [53.163089893876645]
  We propose a novel self-attention mechanism that captures the contextual dependency and temporal relationships within a patient's healthcare journey. An end-to-end bidirectional temporal encoder network (BiteNet) then learns representations of the patient's journeys. We have evaluated the effectiveness of our methods on two supervised prediction and two unsupervised clustering tasks with a real-world EHR dataset.
  arXiv  Detail & Related papers  (2020-09-24T00:42:36Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.