{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,29]],"date-time":"2026-09-29T09:56:44Z","timestamp":1790675804059,"version":"4.1.0"},"reference-count":58,"publisher":"SAGE Publications","issue":"3","license":[{"start":{"date-parts":[[2026,8,12]],"date-time":"2026-08-12T00:00:00Z","timestamp":1786492800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"},{"start":{"date-parts":[[2026,8,12]],"date-time":"2026-08-12T00:00:00Z","timestamp":1786492800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"funder":[{"DOI":"10.13039\/100000049","name":"National Institute on Aging","doi-asserted-by":"publisher","award":["R44AG082605"],"award-info":[{"award-number":["R44AG082605"]}],"id":[{"id":"10.13039\/100000049","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Journal of Alzheimer\u2019s Disease"],"published-print":{"date-parts":[[2026,10]]},"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Approximately half of people living with Alzheimer's disease and related dementias are undiagnosed.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>To develop and validate algorithms that predict risk of undiagnosed dementia using electronic health record (EHR) and\/or healthcare claims data.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>Study participants were adult patients aged 65 years or older without evidence of dementia (diagnosis\/medication) at baseline in two U.S. data sources: 1) Medicare claims (2010 to 2021); 2) EHR and claims from a primary care network (2016 to 2023). We applied coefficients from an existing, validated EHR-based algorithm to predictors defined using Medicare claims and used machine learning to develop new EHR- and claims-based predictive models. We assessed model discrimination using c-statistics.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Study participants included 8,374,400 Medicare beneficiaries (mean [SD] age, 76 [7] years; 57% female) and 29,983 primary care patients (age: 75 [6] years; 56% female). Model discrimination was good when applying EHR-based coefficients to Medicare claims-based predictors (c-statistic [95% confidence interval]: 0.770 [0.767, 0.773]) and was improved by refitting the model (0.795 [0.792, 0.798]) with a small added benefit from incorporating new claims-based predictors (0.801 [0.799; 0.804]). Similarly, when both EHR and claims data were available, discrimination was improved by refitting the model with a small additional benefit from including new predictors, regardless of the data source (EHR, claims, or either).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>A validated EHR-based algorithm predicted risk of undiagnosed dementia in Medicare claims with good discrimination. Model accuracy was improved by refitting and, to a lesser extent, by including novel predictors.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1177\/13872877261474534","type":"journal-article","created":{"date-parts":[[2026,8,12]],"date-time":"2026-08-12T17:54:57Z","timestamp":1786557297000},"page":"1054-1064","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":0,"title":["Development and validation of machine learning models to predict risk of undiagnosed dementia using healthcare claims and electronic health record data"],"prefix":"10.1177","volume":"113","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2953-4079","authenticated-orcid":false,"given":"Deborah E.","family":"Barnes","sequence":"first","affiliation":[{"name":"Department of Psychiatry and Behavioral Sciences, University of California, San Francisco, San Francisco, CA, USA"},{"name":"Department of Epidemiology &amp; Biostatistics, University of California, San Francisco, San Francisco, CA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Cynthia","family":"Benjamin","sequence":"additional","affiliation":[{"name":"Together Senior Health, Inc., San Francisco, CA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"W. 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