{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,29]],"date-time":"2025-12-29T13:14:39Z","timestamp":1767014079594,"version":"3.48.0"},"reference-count":50,"publisher":"Wiley","issue":"4","license":[{"start":{"date-parts":[[2025,11,24]],"date-time":"2025-11-24T00:00:00Z","timestamp":1763942400000},"content-version":"vor","delay-in-days":54,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"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":[[2025,10]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>INTRODUCTION<\/jats:title>\n                    <jats:p>Mild cognitive impairment (MCI) is underdiagnosed by primary care providers (PCPs), with detection rates as low as 6%\u201315%. Predictive models support the identification of individuals at risk, enabling timely intervention.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>METHODS<\/jats:title>\n                    <jats:p>This retrospective study was conducted on 271,054 cognitively unimpaired and 14,501 confirmed MCI cohorts from electronic health records. A machine learning model was developed with a data\u2010driven variable selection approach based on demographics and comorbidities.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>RESULTS<\/jats:title>\n                    <jats:p>From 101 variables, 26 were chosen for the final model, achieving an overall area under the curve (AUC) of 86%. Age\u2010stratified AUCs were 79.1% (40\u201349), 77.0% (50\u201364), 76.9% (65\u201379), and 74.4% (\u226580), showing the highest predictive performance in younger age groups.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>DISCUSSION<\/jats:title>\n                    <jats:p>Demographic factors and comorbidities can serve as effective predictors for the risk of MCI. The model demonstrates strong predictive performance and assists as a triage tool for PCPs, facilitating the identification of individuals with MCI for early treatment.<\/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>Predictive algorithms using electronic health records (EHRs) are useful for identifying individuals at risk for mild cognitive impairment (MCI) to triage for further clinical evaluation.<\/jats:p>\n                        <\/jats:list-item>\n                        <jats:list-item>\n                          <jats:p>A machine learning model was developed using EHR data to predict those at risk for MCI.<\/jats:p>\n                        <\/jats:list-item>\n                        <jats:list-item>\n                          <jats:p>The model identified 26 variables that were able to distinguish the MCI from non\u2010MCI cohorts.<\/jats:p>\n                        <\/jats:list-item>\n                        <jats:list-item>\n                          <jats:p>The model accurately detected MCI across the cohort (area under the curve [AUC]\u00a0=\u00a086%) and trended best for younger age groups (AUC was 77%, 77%, and 74% in 50\u201364, 65\u201379, and \u226580 age groups, respectively).<\/jats:p>\n                        <\/jats:list-item>\n                        <jats:list-item>\n                          <jats:p>Implementation of a triage tool could be used to detect MCI across aging patient populations sooner, leading to a timelier diagnosis, intervention, and treatment management.<\/jats:p>\n                        <\/jats:list-item>\n                      <\/jats:list>\n                    <\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/dad2.70209","type":"journal-article","created":{"date-parts":[[2025,11,25]],"date-time":"2025-11-25T07:52:14Z","timestamp":1764057134000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Risk prediction of mild cognitive impairment using electronic health record data"],"prefix":"10.1002","volume":"17","author":[{"given":"Gang","family":"Li","sequence":"first","affiliation":[{"name":"Global Medical Affairs Eisai Inc.  Nutley New Jersey USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bryan","family":"Cobb","sequence":"additional","affiliation":[{"name":"Global Medical Affairs Eisai Inc.  Nutley New Jersey USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Todd M","family":"Nelson","sequence":"additional","affiliation":[{"name":"Global Medical Affairs Eisai Inc.  Nutley New Jersey USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Viswanath","family":"Devanarayan","sequence":"additional","affiliation":[{"name":"Global Medical Affairs Eisai Inc.  Nutley New Jersey USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Stephane","family":"Borentain","sequence":"additional","affiliation":[{"name":"Global Medical Affairs Eisai Inc.  Nutley New Jersey USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Michelle M.","family":"Mielke","sequence":"additional","affiliation":[{"name":"Department of Epidemiology and Prevention Wake Forest University School of Medicine  Winston\u2010Salem North Carolina USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"James E","family":"Galvin","sequence":"additional","affiliation":[{"name":"Comprehensive Center for Brain Health, Department of Neurology University of Miami Miller School of Medicine  Miami Florida USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Miia","family":"Kivipelto","sequence":"additional","affiliation":[{"name":"Division of Clinical Geriatrics, Department of Neurobiology, Care Sciences and Society Karolinska Institute  Stockholm Sweden"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rifky","family":"Tkatch","sequence":"additional","affiliation":[{"name":"Global Medical Affairs Eisai Inc.  Nutley New Jersey USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Susan","family":"De Santi","sequence":"additional","affiliation":[{"name":"Global Medical Affairs Eisai Inc.  Nutley New Jersey USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Feride","family":"Frech","sequence":"additional","affiliation":[{"name":"Global Medical Affairs Eisai Inc.  Nutley New Jersey USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jo","family":"Vandercappellen","sequence":"additional","affiliation":[{"name":"Global Medical Affairs Eisai Inc.  Nutley New Jersey USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yosuke","family":"Nakamura","sequence":"additional","affiliation":[{"name":"Global Medical Affairs Eisai Inc.  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