{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,18]],"date-time":"2026-08-18T13:24:29Z","timestamp":1787059469081,"version":"build-2736575974"},"reference-count":69,"publisher":"SAGE Publications","issue":"4","license":[{"start":{"date-parts":[[2026,1,13]],"date-time":"2026-01-13T00:00:00Z","timestamp":1768262400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Journal of Alzheimer\u2019s Disease"],"published-print":{"date-parts":[[2026,2]]},"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Alzheimer's disease (AD) patients experience elevated mortality during emergency department (ED) encounters, yet the associated risk factors remain insufficiently characterized.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>To identify predictors of mortality among older adults with AD during ED visits and examine differences in comorbidity patterns between those who died and those who survived.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We analyzed 20,532,351 ED visits for adults aged \u226560 from the 2012\u20132014 Nationwide Emergency Department Sample (NEDS). Visits were stratified by AD status, ZIP-code income quartile, and mortality outcome (defined as in-ED or in-hospital death following ED presentation). We used logistic regression and machine learning models (random forest, gradient boosting, XGBoost) to predict mortality in a 1:1 matched case-control dataset. SHapley Additive exPlanations (SHAP) were applied to interpret model outputs.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>AD patients accounted for 1.76\u20132.13% of all ED visits, with mortality rates of 2.55\u20132.68% compared to 1.10\u20131.76% for non-AD patients. Socioeconomic status and ED charges were not associated with increased mortality. Odds ratio analysis identified rare terminal events as top predictors (e.g., respiratory arrest, OR = 55.5), while SHAP analysis highlighted more prevalent and clinically actionable conditions such as acute respiratory failure and septicemia as major drivers of mortality. All models performed comparably (AUC \u2248 0.85).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>AD patients face significantly higher mortality during ED encounters. Integrating explainable machine learning with large-scale administrative data may help flag lethal comorbidities in real time and improve outcomes through better ED triage and care prioritization.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1177\/13872877251409332","type":"journal-article","created":{"date-parts":[[2026,1,13]],"date-time":"2026-01-13T09:19:47Z","timestamp":1768295987000},"page":"1674-1687","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":0,"title":["Flagging high-risk comorbidities in Alzheimer's disease emergency department visits: A machine learning analysis of mortality outcomes"],"prefix":"10.1177","volume":"109","author":[{"ORCID":"https:\/\/orcid.org\/0009-0001-4150-8383","authenticated-orcid":false,"given":"Tursun","family":"Alkam","sequence":"first","affiliation":[{"name":"Master's Program of Applied Artificial Intelligence, University of San Diego, San Diego, CA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ebrahim","family":"Tarshizi","sequence":"additional","affiliation":[{"name":"Master's Program of Applied Artificial Intelligence, University of San Diego, San Diego, CA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Andrew H.","family":"Van Benschoten","sequence":"additional","affiliation":[{"name":"Master's Program of Applied Artificial Intelligence, University of San Diego, San Diego, CA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","published-online":{"date-parts":[[2026,1,13]]},"reference":[{"key":"e_1_3_3_2_2","doi-asserted-by":"publisher","DOI":"10.3389\/fmed.2024.1474043"},{"key":"e_1_3_3_3_2","doi-asserted-by":"publisher","DOI":"10.1002\/alz.13809"},{"key":"e_1_3_3_4_2","doi-asserted-by":"publisher","DOI":"10.1097\/WAD.0000000000000118"},{"key":"e_1_3_3_5_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jpainsymman.2024.02.203"},{"key":"e_1_3_3_6_2","doi-asserted-by":"publisher","DOI":"10.1136\/bmjopen-2023-075022"},{"key":"e_1_3_3_7_2","doi-asserted-by":"publisher","DOI":"10.4061\/2011\/840312"},{"key":"e_1_3_3_8_2","doi-asserted-by":"publisher","DOI":"10.5455\/msm.2018.30.221-224"},{"key":"e_1_3_3_9_2","doi-asserted-by":"publisher","DOI":"10.1101\/cshperspect.a006148"},{"key":"e_1_3_3_10_2","first-page":"327","article-title":"Effects of electronic health record implementation and barriers to adoption and use: a scoping review and qualitative analysis of the content","volume":"10","author":"Tsai CH","year":"2020","unstructured":"Tsai CH, Eghdam A, Davoody N, et al. 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