{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T10:59:54Z","timestamp":1785581994507,"version":"3.56.0"},"reference-count":31,"publisher":"Wiley","issue":"2","license":[{"start":{"date-parts":[[2026,4,8]],"date-time":"2026-04-08T00:00:00Z","timestamp":1775606400000},"content-version":"vor","delay-in-days":7,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"},{"start":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T00:00:00Z","timestamp":1775001600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"funder":[{"DOI":"10.13039\/501100001505","name":"Health Research Council of New Zealand","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100001505","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["alz-journals.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Alz &amp;amp; Dem Diag Ass &amp;amp; Dis Mo"],"published-print":{"date-parts":[[2026,4]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Introduction<\/jats:title>\n                    <jats:p>Timely identification of dementia remains a major clinical challenge globally, with many cases being unrecognized. This study evaluated whether using machine learning models with routinely collected health data can support dementia case finding.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Method<\/jats:title>\n                    <jats:p>De\u2010identified datasets were used to create a nested case\u2013control of 8195 individuals with dementia and 8195 matched controls. Four models incorporating both cross\u2010sectional and longitudinal features were developed and tested.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The best\u2010performing model achieved an area under the curve of 0.86 (95% confidence interval [CI]: 0.84\u20130.87), with sensitivity of 73.3% (95% CI: 72.3\u201374.2) and specificity of 87.5% (95% CI: 86.7\u201388.2). Key predictors included time\u2010stamped International Classification of Diseases 10th Revision diagnostic codes, health\u2010care use, referral to aged residential care, and hospital delirium assessments.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Discussion<\/jats:title>\n                    <jats:p>A unique feature was the inclusion of \u201ctimestamp data\u201d that allowed us to assess the longitudinal changes which may have improved performance of the model. These findings demonstrate the potential for using machine learning with routine health data to enhance early dementia detection.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Highlights<\/jats:title>\n                    <jats:p>\n                      <jats:list list-type=\"bullet\">\n                        <jats:list-item>\n                          <jats:p>Machine learning models using routine health data in a real\u2010world setting in New Zealand had good accuracy for identification of dementia (area under the curve 0.86, sensitivity 73.3%, and specificity 87.5%).<\/jats:p>\n                        <\/jats:list-item>\n                        <jats:list-item>\n                          <jats:p>Longitudinal sequential \u201ctimestamp\u201d data is a unique feature that improved the performance of machine learning models for dementia case finding.<\/jats:p>\n                        <\/jats:list-item>\n                        <jats:list-item>\n                          <jats:p>Key predictors included International Classification of Diseases 10th Revision codes, health\u2010care use, referrals to aged residential care, and positive delirium scores.<\/jats:p>\n                        <\/jats:list-item>\n                      <\/jats:list>\n                    <\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/dad2.70332","type":"journal-article","created":{"date-parts":[[2026,4,8]],"date-time":"2026-04-08T16:27:24Z","timestamp":1775665644000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Advancing dementia identification using machine learning and real\u2010world sequential health data"],"prefix":"10.1002","volume":"18","author":[{"given":"Cristian","family":"Gonzalez\u2010Prieto","sequence":"first","affiliation":[{"name":"School of Computer Science University of Auckland  Auckland New 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