{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,12]],"date-time":"2026-01-12T09:11:16Z","timestamp":1768209076504,"version":"3.49.0"},"reference-count":36,"publisher":"The Journal of Health Economics and Outcomes Research","issue":"2","license":[{"start":{"date-parts":[[2024,9,25]],"date-time":"2024-09-25T00:00:00Z","timestamp":1727222400000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["jheor.org"],"crossmark-restriction":true},"short-container-title":[],"abstract":"<jats:sec id=\"background\"> <jats:title>Background<\/jats:title> <jats:p>Although increasing in prevalence, nonalcoholic steatohepatitis (NASH) is often undiagnosed in clinical practice.<\/jats:p> <\/jats:sec> <jats:sec id=\"objective\"> <jats:title>Objective<\/jats:title> <jats:p>This study identified patients in the Veterans Affairs (VA) health system who likely had undiagnosed NASH using a machine learning algorithm.<\/jats:p> <\/jats:sec> <jats:sec id=\"methods\"> <jats:title>Methods<\/jats:title> <jats:p>From a VA data set of 25 million adult enrollees, the study population was divided into NASH-positive, non-NASH, and at-risk cohorts. We performed a claims data analysis using a machine learning algorithm. To build our model, the study population was randomly divided into an 80% training subset and a 20% testing subset and tested and trained using a cross-validation technique. In addition to the baseline model, a gradient-boosted classification tree, na\u00efve Bayes, and random forest model were created and compared using receiver operator characteristics, area under the curve, and accuracy. The best performing model was retrained on the full 80% training subset and applied to the 20% testing subset to calculate the performance metrics.<\/jats:p> <\/jats:sec> <jats:sec id=\"results\"> <jats:title>Results<\/jats:title> <jats:p>In total, 4\u202f223\u202f443 patients met the study inclusion criteria, of whom 4903 were positive for NASH and 35\u202f528 were non-NASH patients. The remainder was in the at-risk patient cohort, of which 514\u202f997 patients (12%) were identified as likely to have NASH. Age, obesity, and abnormal liver function tests were the top determinants in assigning NASH probability.<\/jats:p> <\/jats:sec> <jats:sec id=\"conclusions\"> <jats:title>Conclusions<\/jats:title> <jats:p>Utilization of machine learning to predict NASH allows for wider recognition, timely intervention, and targeted treatments to improve or mitigate disease progression and could be used as an initial screening tool.<\/jats:p> <\/jats:sec>","DOI":"10.36469\/001c.123645","type":"journal-article","created":{"date-parts":[[2024,9,25]],"date-time":"2024-09-25T11:28:35Z","timestamp":1727263715000},"update-policy":"https:\/\/doi.org\/10.36469\/00001","source":"Crossref","is-referenced-by-count":1,"title":["Artificial Intelligence in Identifying Patients With Undiagnosed Nonalcoholic Steatohepatitis"],"prefix":"10.36469","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7447-5672","authenticated-orcid":false,"given":"Onur","family":"Baser","sequence":"first","affiliation":[{"name":"Graduate School of Public Health, City University of New York, New York, NY, USA"},{"name":"University of Michigan Medical School, Ann Arbor, Michigan, USA"},{"name":"John D. Dingell VA Center, Detroit, Michigan, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Gabriela","family":"Samayoa","sequence":"additional","affiliation":[{"name":"Columbia Data Analytics, Ann Arbor, Michigan, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nehir","family":"Yapar","sequence":"additional","affiliation":[{"name":"Columbia Data Analytics, Ann Arbor, Michigan, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Erdem","family":"Baser","sequence":"additional","affiliation":[{"name":"Columbia Data Analytics, Ann Arbor, Michigan, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"22273","published-online":{"date-parts":[[2024,9,25]]},"reference":[{"key":"ref1","first-page":"1","article-title":"Nonalcoholic fatty liver disease underdiagnosis in primary care: what are we missing?","volume":"2022","author":"E. M. 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