{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,30]],"date-time":"2025-07-30T09:59:13Z","timestamp":1753869553108,"version":"3.41.2"},"reference-count":33,"publisher":"Wiley","issue":"3","license":[{"start":{"date-parts":[[2018,6,4]],"date-time":"2018-06-04T00:00:00Z","timestamp":1528070400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100008982","name":"National Science Foundation","doi-asserted-by":"publisher","award":["CNS 0964392","CNS 1330491","0964063","1526014"],"award-info":[{"award-number":["CNS 0964392","CNS 1330491","0964063","1526014"]}],"id":[{"id":"10.13039\/501100008982","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000016","name":"U.S. Department of Health and Human Services","doi-asserted-by":"publisher","award":["90TR0003\u201001"],"award-info":[{"award-number":["90TR0003\u201001"]}],"id":[{"id":"10.13039\/100000016","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Learning Health Systems"],"published-print":{"date-parts":[[2018,7]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>A medical specialty indicates the skills needed by health care providers to conduct key procedures or make critical judgments. However, documentation about specialties may be lacking or inaccurately specified in a health care institution. Thus, we propose to leverage diagnosis histories to recognize medical specialties that exist in practice. Such specialties that are highly recognizable through diagnosis histories are de facto diagnosis specialties. We aim to recognize de facto diagnosis specialties that are listed in the Health Care Provider Taxonomy Code Set (HPTCS) and discover those that are unlisted. First, to recognize the former, we use similarity and supervised learning models. Next, to discover de facto diagnosis specialties unlisted in the HPTCS, we introduce a general discovery\u2010evaluation framework. In this framework, we use a semi\u2010supervised learning model and an unsupervised learning model, from which the discovered specialties are subsequently evaluated by the similarity and supervised learning models used in recognition. To illustrate the potential for these approaches, we collect 2 data sets of 1\u00a0year of diagnosis histories from a large academic medical center: One is a subset of the other except for additional information useful for network analysis. The results indicate that 12 core de facto diagnosis specialties listed in the HPTCS are highly recognizable. Additionally, the semi\u2010supervised learning model discovers a specialty for breast cancer on the smaller data set based on network analysis, while the unsupervised learning model confirms this discovery and suggests an additional specialty for <jats:italic>Obesity<\/jats:italic> on the larger data set. The potential correctness of these 2 specialties is reinforced by the evaluation results that they are highly recognizable by similarity and supervised learning models in comparison with 12 core de facto diagnosis specialties listed in the HPTCS.<\/jats:p>","DOI":"10.1002\/lrh2.10057","type":"journal-article","created":{"date-parts":[[2018,6,5]],"date-time":"2018-06-05T02:15:32Z","timestamp":1528164932000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["De facto diagnosis specialties: <scp>R<\/scp>ecognition and discovery"],"prefix":"10.1002","volume":"2","author":[{"given":"Aston","family":"Zhang","sequence":"first","affiliation":[{"name":"Department of Computer Science University of Illinois at Urbana\u2010Champaign  Urbana Illinois"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xun","family":"Lu","sequence":"additional","affiliation":[{"name":"Department of Computer Science University of Illinois at Urbana\u2010Champaign  Urbana Illinois"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Carl A.","family":"Gunter","sequence":"additional","affiliation":[{"name":"Department of Computer Science University of Illinois at Urbana\u2010Champaign  Urbana Illinois"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shuochao","family":"Yao","sequence":"additional","affiliation":[{"name":"Department of Computer Science University of Illinois at Urbana\u2010Champaign  Urbana Illinois"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fangbo","family":"Tao","sequence":"additional","affiliation":[{"name":"Department of Computer Science University of Illinois at Urbana\u2010Champaign  Urbana Illinois"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rongda","family":"Zhu","sequence":"additional","affiliation":[{"name":"Department of Computer Science University of Illinois at Urbana\u2010Champaign  Urbana Illinois"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Huan","family":"Gui","sequence":"additional","affiliation":[{"name":"Department of Computer Science University of Illinois at Urbana\u2010Champaign  Urbana Illinois"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Daniel","family":"Fabbri","sequence":"additional","affiliation":[{"name":"Department of Biomedical Informatics Vanderbilt University  Nashville Tennessee"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"David","family":"Liebovitz","sequence":"additional","affiliation":[{"name":"Department of Medicine University of Chicago  Chicago Illinois"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bradley","family":"Malin","sequence":"additional","affiliation":[{"name":"Department of Biomedical Informatics Vanderbilt University  Nashville Tennessee"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2018,6,4]]},"reference":[{"key":"e_1_2_10_2_1","doi-asserted-by":"crossref","unstructured":"GuptaS HansonC GunterC FrankM LiebovitzD MalinB.Modeling and detecting anomalous topic access. 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