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Most existing methods fall short in one of these 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 article proposes\n                    <jats:monospace>HypeMed<\/jats:monospace>\n                    , a two-stage hypergraph-based framework unifying intra-visit coherence modeling and inter-visit augmentation.\n                    <jats:monospace>HypeMed<\/jats:monospace>\n                    consists of two components: MedRep for representation pretraining and SimMR for similarity-enhanced recommendation. In the first stage, MedRep encodes clinical visits as hyperedges via knowledge-aware contrastive pretraining, 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\u2019s longitudinal data to refine medication prediction. Evaluation on real-world benchmarks shows that\n                    <jats:monospace>HypeMed<\/jats:monospace>\n                    outperforms state-of-the-art baselines in both recommendation precision and DDI reduction, simultaneously enhancing the effectiveness and safety of clinical decision support. The implementation is publicly available at\n                    <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" ext-link-type=\"uri\" xlink:href=\"https:\/\/github.com\/xansar\/HypeMed\">https:\/\/github.com\/xansar\/HypeMed<\/jats:ext-link>\n                    .\n                  <\/jats:p>","DOI":"10.1145\/3803851","type":"journal-article","created":{"date-parts":[[2026,3,27]],"date-time":"2026-03-27T14:22:47Z","timestamp":1774621367000},"page":"1-28","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["HypeMed: Enhancing Medication Recommendations with Hypergraph-Based Patient Relationships"],"prefix":"10.1145","volume":"44","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-0306-8168","authenticated-orcid":false,"given":"Xiangxu","family":"Zhang","sequence":"first","affiliation":[{"name":"Gaoling School of Artificial Intelligence, Renmin University of China, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0868-764X","authenticated-orcid":false,"given":"Xiao","family":"Zhou","sequence":"additional","affiliation":[{"name":"Gaoling School of Artificial Intelligence, Renmin University of China, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4192-5360","authenticated-orcid":false,"given":"Hongteng","family":"Xu","sequence":"additional","affiliation":[{"name":"Gaoling School of Artificial Intelligence, Renmin University of China, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3108-5601","authenticated-orcid":false,"given":"Jianxun","family":"Lian","sequence":"additional","affiliation":[{"name":"Microsoft Research Asia, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2026,5,11]]},"reference":[{"key":"e_1_3_3_2_2","doi-asserted-by":"publisher","DOI":"10.1145\/3488668"},{"key":"e_1_3_3_3_2","doi-asserted-by":"crossref","unstructured":"David Blumenthal Elizabeth J. 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