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Med."],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>\n                    Determining acute ischemic stroke (AIS) etiology is fundamental to secondary stroke prevention efforts but can be diagnostically challenging. We trained and validated an automated classification tool,\n                    <jats:italic>StrokeClassifier<\/jats:italic>\n                    , using electronic health record (EHR) text from 2039 non-cryptogenic AIS patients at 2 academic hospitals to predict the 4-level outcome of stroke etiology adjudicated by agreement of at least 2 board-certified vascular neurologists\u2019 review of the EHR.\n                    <jats:italic>StrokeClassifier<\/jats:italic>\n                    is an ensemble consensus meta-model of 9 machine learning classifiers applied to features extracted from discharge summary texts by natural language processing.\n                    <jats:italic>StrokeClassifier<\/jats:italic>\n                    was externally validated in 406 discharge summaries from the MIMIC-III dataset reviewed by a vascular neurologist to ascertain stroke etiology. Compared with vascular neurologists\u2019 diagnoses,\n                    <jats:italic>StrokeClassifier<\/jats:italic>\n                    achieved the mean cross-validated accuracy of 0.74 and weighted F1 of 0.74 for multi-class classification. In MIMIC-III, its accuracy and weighted F1 were 0.70 and 0.71, respectively. In binary classification, the two metrics ranged from 0.77 to 0.96. The top 5 features contributing to stroke etiology prediction were atrial fibrillation, age, middle cerebral artery occlusion, internal carotid artery occlusion, and frontal stroke location. We designed a certainty heuristic to grade the confidence of\n                    <jats:italic>StrokeClassifier\u2019s<\/jats:italic>\n                    diagnosis as non-cryptogenic by the degree of consensus among the 9 classifiers and applied it to 788 cryptogenic patients, reducing cryptogenic diagnoses from 25.2% to 7.2%.\n                    <jats:italic>StrokeClassifier<\/jats:italic>\n                    is a validated artificial intelligence tool that rivals the performance of vascular neurologists in classifying ischemic stroke etiology. With further training,\n                    <jats:italic>StrokeClassifier<\/jats:italic>\n                    may have downstream applications including its use as a clinical decision support system.\n                  <\/jats:p>","DOI":"10.1038\/s41746-024-01120-w","type":"journal-article","created":{"date-parts":[[2024,5,17]],"date-time":"2024-05-17T11:02:24Z","timestamp":1715943744000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":20,"title":["StrokeClassifier: ischemic stroke etiology classification by ensemble consensus modeling using electronic health records"],"prefix":"10.1038","volume":"7","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3616-5387","authenticated-orcid":false,"given":"Ho-Joon","family":"Lee","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0592-9145","authenticated-orcid":false,"given":"Lee H.","family":"Schwamm","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lauren H.","family":"Sansing","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hooman","family":"Kamel","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Adam","family":"de Havenon","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ashby C.","family":"Turner","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2003-5473","authenticated-orcid":false,"given":"Kevin N.","family":"Sheth","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5823-1985","authenticated-orcid":false,"given":"Smita","family":"Krishnaswamy","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Cynthia","family":"Brandt","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1195-9607","authenticated-orcid":false,"given":"Hongyu","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2046-127X","authenticated-orcid":false,"given":"Harlan","family":"Krumholz","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0798-2353","authenticated-orcid":false,"given":"Richa","family":"Sharma","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,5,17]]},"reference":[{"key":"1120_CR1","doi-asserted-by":"publisher","first-page":"e56","DOI":"10.1161\/CIR.0000000000000659","volume":"139","author":"EJ Benjamin","year":"2019","unstructured":"Benjamin, E. 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H.L. reports a consulting role at Guidepoint outside of this submitted work. H.M.K. works under contract with the Centers for Medicare & Medicaid Services to support quality measurement programs, was a recipient of a research grant from Johnson & Johnson, through Yale University, to support clinical trial data sharing; was a recipient of a research agreement, through Yale University, from the Shenzhen Center for Health Information for work to advance intelligent disease prevention and health promotion; collaborates with the National Center for Cardiovascular Diseases in Beijing; receives payment from the Arnold & Porter Law Firm for work related to the Sanofi clopidogrel litigation, from the Martin Baughman Law Firm for work related to the Cook Celect IVC filter litigation, and from the Siegfried and Jensen Law Firm for work related to Vioxx litigation; chairs a Cardiac Scientific Advisory Board for UnitedHealth; was a member of the IBM Watson Health Life Sciences Board; is a member of the Advisory Board for Element Science, the Advisory Board for Facebook, and the Physician Advisory Board for Aetna; and is the co-founder of Hugo Health, a personal health information platform, and co-founder of Refactor Health, a healthcare AI-augmented data management company. A.D.H. has received consultant fees from Integra and Novo Nordisk, has equity in TitinKM and Certus, and receives author fees from <i>UpToDate<\/i>. K.N.S. reports investigator\u2010initiated clinical research funding to Yale from Hyperfine, Inc., Biogen, and Bard; reports from Sense and Zoll for data and safety monitoring services; compensation from Cerevasc for consultant services; compensation from Rhaeos for consultant services, compensation from Certus for consultant services; and a patent pending for Stroke wearables licensed to Alva Health. S.K. is on the scientific advisory board of KovaDx and AI Therapeutics. H.K. reports compensation from Novo Nordisk for end-point review committee services, compensation from Medtronic for other services, compensation from Janssen Biotech for other services, compensation from Boehringer Ingelheim for end-point review committee services, and employment by Weill Cornell Medical College. L.H.S. reports compensation as a scientific consultant regarding trial design and conduct on late window thrombolysis and member of steering committee for Genentech (TIMELESS NCT03785678); user interface design and usability to LifeImage (privately held teleradiology company); member of a Data Safety Monitoring Board (DSMB) for Penumbra (MIND NCT03342664; PI, multicenter trial of stroke prevention in atrial fibrillation for Medtronic (Stroke AF NCT02700945). The remaining authors declare no competing interests.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"130"}}