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We have previously proposed that deliberately exploring diseases at a moderate semantic distance, that is, diseases moderately similar to the pivot, may foster diagnostic creativity. Building on that proposal, this paper specifies it as a rank-defined, auditable research programme, drawing on two established methods: ranking candidate diagnoses by embedding-based similarity, and building disease vectors from clinical presentation, not disease names. Each disease is represented as a disease concept vector and positioned within a high-dimensional semantic space (the\u00a0disease map). Diseases with similar clinical features are thereby intended to lie close together, a testable design rather than an established property. Candidate diagnoses are ranked by cosine similarity to a given pivot disease. Because training is stochastic and corpus-dependent, the ranking is auditable rather than strictly reproducible. Taking a clinician-generated pivot as input, the framework supports bounded exploration of moderate-distance candidates; it does not generate the pivot. The disease map is offered as a research agenda rather than a validated tool, and we outline diagnosis-referenced evaluation of map-level retrieval validity, followed by prospective testing of its effects on clinicians\u2019 hypothesis generation and diagnostic performance.<\/jats:p>","DOI":"10.1515\/dx-2026-0124","type":"journal-article","created":{"date-parts":[[2026,10,12]],"date-time":"2026-10-12T19:56:11Z","timestamp":1791834971000},"source":"Crossref","is-referenced-by-count":0,"title":["Semantic distance for hypothesis generation in differential diagnosis: a framework to support the pivot and cluster strategy"],"prefix":"10.1515","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2163-4841","authenticated-orcid":false,"given":"Sho","family":"Isoda","sequence":"first","affiliation":[{"name":"Department of General Medicine , Osaka Medical and Pharmaceutical University Hospital , Takatsuki , Osaka , Japan"},{"name":"Psychology Division, Graduate School of Human Science , Ritsumeikan University , Ibaraki , Osaka , Japan"},{"name":"Japan Diagnostic Excellence Team , Chuo-ku , Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9723-3550","authenticated-orcid":false,"given":"Tadayuki","family":"Hashimoto","sequence":"additional","affiliation":[{"name":"Department of General Medicine , Osaka Medical and Pharmaceutical University Hospital , Takatsuki , Osaka , Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3788-487X","authenticated-orcid":false,"given":"Taro","family":"Shimizu","sequence":"additional","affiliation":[{"name":"Japan Diagnostic Excellence Team , Chuo-ku , Japan"},{"name":"Department of Diagnostic and Generalist Medicine , Dokkyo Medical University Hospital , Mibu , Tochigi , Japan"},{"name":"Harvard Medical School , Boston , MA , USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4652-6994","authenticated-orcid":false,"given":"Masahide","family":"Koda","sequence":"additional","affiliation":[{"name":"Division of Epidemiology, School of Public Health , Tohoku University Graduate School of Medicine , Sendai , Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6974-6906","authenticated-orcid":false,"given":"Masasi","family":"Hattori","sequence":"additional","affiliation":[{"name":"College of Comprehensive Psychology , Ritsumeikan University , Ibaraki , Osaka , Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tomio","family":"Suzuki","sequence":"additional","affiliation":[{"name":"Department of General Medicine , Osaka Medical and Pharmaceutical University Hospital , Takatsuki , Osaka , Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"374","published-online":{"date-parts":[[2026,10,14]]},"reference":[{"key":"2026110316553487362_j_dx-2026-0124_ref_001","unstructured":"Slater, TA, Abdul Waduud, M, Ahmed, N. 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Linguistic regularities in continuous space word representations. In: Proceedings of the 2013 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Association for Computational Linguistics, Stroudsburg (PA); 2013:746\u201351 pp."},{"key":"2026110316553487362_j_dx-2026-0124_ref_026","doi-asserted-by":"crossref","unstructured":"Nissim, M, van Noord, R, van der Goot, R. Fair is better than sensational: man is to doctor as woman is to doctor. Comput Linguist 2020;46:487\u201397. https:\/\/doi.org\/10.1162\/coli_a_00379.","DOI":"10.1162\/coli_a_00379"},{"key":"2026110316553487362_j_dx-2026-0124_ref_027","unstructured":"Schakel, AMJ, Wilson, BJ. Measuring word significance using distributed representations of words. arXiv [Preprint] 2015. arXiv:1508.02297 https:\/\/doi.org\/10.48550\/arXiv.1508.02297."},{"key":"2026110316553487362_j_dx-2026-0124_ref_028","unstructured":"Tessari, F, Yao, K, Hogan, N. Surpassing cosine similarity for multidimensional comparisons: dimension insensitive Euclidean metric. arXiv [Preprint] 2024. arXiv:2407.08623 https:\/\/doi.org\/10.48550\/arXiv.2407.08623."},{"key":"2026110316553487362_j_dx-2026-0124_ref_029","doi-asserted-by":"crossref","unstructured":"J\u00e4rvelin, K, Kek\u00e4l\u00e4inen, J. Cumulated gain-based evaluation of IR techniques. ACM Trans Inf Syst 2002;20:422\u201346. https:\/\/doi.org\/10.1145\/582415.582418.","DOI":"10.1145\/582415.582418"},{"key":"2026110316553487362_j_dx-2026-0124_ref_030","doi-asserted-by":"crossref","unstructured":"Voorhees, EM. The TREC-8 question answering track report. In: Proceedings of the 8th Text REtrieval Conference (TREC-8), National Institute of Standards and Technology, Gaithersburg (MD);1999:77\u201382 pp.","DOI":"10.6028\/NIST.SP.500-246.qa-overview"},{"key":"2026110316553487362_j_dx-2026-0124_ref_031","doi-asserted-by":"crossref","unstructured":"Tran, LT, Divita, G, Carter, ME, Judd, J, Samore, MH, Gundlapalli, AV. Exploiting the UMLS Metathesaurus for extracting and categorizing concepts representing signs and symptoms to anatomically related organ systems. J\u00a0Biomed Inf 2015;58:19\u201327. https:\/\/doi.org\/10.1016\/j.jbi.2015.08.024.","DOI":"10.1016\/j.jbi.2015.08.024"},{"key":"2026110316553487362_j_dx-2026-0124_ref_032","doi-asserted-by":"crossref","unstructured":"Isoda, S, Shimizu, T, Hashimoto, T, Hattori, M, Suzuki, T. Creativity and diagnostic reasoning. Diagnosis (Berl) 2025;13:175\u201380. https:\/\/doi.org\/10.1515\/dx-2025-0015.","DOI":"10.1515\/dx-2025-0015"},{"key":"2026110316553487362_j_dx-2026-0124_ref_033","doi-asserted-by":"crossref","unstructured":"Nessa, J, Malterud, K. \u201cFeeling your large intestines a bit bound\u201d: clinical interaction, talk and gaze. 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