{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,16]],"date-time":"2026-10-16T17:21:27Z","timestamp":1792171287875,"version":"4.4.0"},"reference-count":55,"publisher":"Wiley","issue":"6","license":[{"start":{"date-parts":[[2025,6,2]],"date-time":"2025-06-02T00:00:00Z","timestamp":1748822400000},"content-version":"vor","delay-in-days":1,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["alz-journals.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Alzheimer's &amp;amp; Dementia"],"published-print":{"date-parts":[[2025,6]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>INTRODUCTION<\/jats:title><jats:p>The study aimed to develop and validate the Emergency Department Dementia Algorithm (EDDA) to detect dementia among older adults (65+) and support clinical decision\u2010making in the emergency department (ED).<\/jats:p><\/jats:sec><jats:sec><jats:title>METHODS<\/jats:title><jats:p>In a multisite retrospective study of 759,665 ED visits, electronic health record data from Yale New Haven Health (2014\u20132022) were used to train three supervised and semi\u2010unsupervised positive\u2010unlabeled machine learning models (XGBoost, Random Forest, LASSO). A separate test set of 400 ED encounters underwent adjudicated chart review for validation.<\/jats:p><\/jats:sec><jats:sec><jats:title>RESULTS<\/jats:title><jats:p>EDDA achieved an area under the receiver\u2010operating characteristic curve (AUROC) of 0.85 in the test set and 0.93 in the validation set. Positive\u2010unlabeled learning improved performance. Agreement between EDDA and clinician\u2010adjudicated dementia diagnoses was moderate (kappa\u00a0=\u00a00.50), with 17% of EDDA\u2010positive patients having undiagnosed probable dementia.<\/jats:p><\/jats:sec><jats:sec><jats:title>DISCUSSION<\/jats:title><jats:p>EDDA enhances dementia detection in the ED, with potential for real\u2010time implementation to improve patient outcomes and care transitions.<\/jats:p><\/jats:sec><jats:sec><jats:title>Highlights<\/jats:title><jats:p><jats:list list-type=\"bullet\">\n<jats:list-item><jats:p>Developed a machine learning algorithm using electronic health record data to detect dementia in the emergency department (ED).<\/jats:p><\/jats:list-item>\n<jats:list-item><jats:p>Algorithm designed to balance detection accuracy with ease of ED implementation.<\/jats:p><\/jats:list-item>\n<jats:list-item><jats:p>Parsimonious model with limited but predictive variables selected for rapid ED use.<\/jats:p><\/jats:list-item>\n<jats:list-item><jats:p>Focused on real\u2010time application, optimizing ED workflows, and clinician support.<\/jats:p><\/jats:list-item>\n<jats:list-item><jats:p>Aims to enhance ED dementia detection, patient safety, and care coordination.<\/jats:p><\/jats:list-item>\n<\/jats:list><\/jats:p><\/jats:sec>","DOI":"10.1002\/alz.70334","type":"journal-article","created":{"date-parts":[[2025,6,3]],"date-time":"2025-06-03T02:39:01Z","timestamp":1748918341000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Detection of emergency department patients at risk of dementia through artificial intelligence"],"prefix":"10.1002","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5807-2635","authenticated-orcid":false,"given":"Inessa","family":"Cohen","sequence":"first","affiliation":[{"name":"Department of Emergency Medicine Yale School of Medicine  New Haven Connecticut USA"},{"name":"Department of Biomedical Informatics and Data Science Yale 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