{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,3]],"date-time":"2026-06-03T20:07:10Z","timestamp":1780517230007,"version":"3.54.1"},"reference-count":45,"publisher":"Oxford University Press (OUP)","issue":"6","license":[{"start":{"date-parts":[[2026,3,17]],"date-time":"2026-03-17T00:00:00Z","timestamp":1773705600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/pages\/standard-publication-reuse-rights"}],"funder":[{"DOI":"10.13039\/100000049","name":"National Institute on Aging","doi-asserted-by":"publisher","award":["P30 AG 017265"],"award-info":[{"award-number":["P30 AG 017265"]}],"id":[{"id":"10.13039\/100000049","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,5,7]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Predicting Alzheimer\u2019s disease (AD)-related cognitive impairment (CI) among cognitively normal (CN) adults enables meaningful disease modification through early intervention and enrichment of clinical trials.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>A deep survival model is trained to predict CI conversion risk in 1415 CN adults from the National Alzheimer\u2019s Coordinating Center. Converters\u2019 (N\u2009=\u2009212) and non-converters\u2019 (N\u2009=\u20091203) baseline clinical measures and magnetic resonance images are used to estimate their conversion probability up to 22\u2009years after baseline observation.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>After 20-fold cross-validation, the model predicts conversion probability with a c-index of 0.88, classification accuracy of 75%, and AUC ROC of 0.89, outperforming previous machine learning models.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>This is one of few studies on the important challenge of predicting future CI among unimpaired subjects. Deep survival modeling can improve the identification of preclinical AD and suggests that uncertainty in AD risk estimation is due to potentially modifiable lifestyle factors.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1093\/gerona\/glag076","type":"journal-article","created":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T03:50:56Z","timestamp":1773805856000},"source":"Crossref","is-referenced-by-count":0,"title":["Deep survival modeling to predict future cognitive impairment in unimpaired adults"],"prefix":"10.1093","volume":"81","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7205-4177","authenticated-orcid":false,"given":"Phoebe","family":"Imms","sequence":"first","affiliation":[{"name":"Leonard Davis School of Gerontology, University of Southern California , Los Angeles, California,","place":["United States"]},{"name":"Translational Neurogenomics Laboratory, QIMR Berghofer Medical Research Institute , Brisbane, Queensland,","place":["Australia"]},{"name":"The School of Biomedical Sciences, The University of Queensland , Brisbane, Queensland,","place":["Australia"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-3929-7864","authenticated-orcid":false,"given":"Haoqing","family":"Wang","sequence":"additional","affiliation":[{"name":"Leonard Davis School of Gerontology, University of Southern California , Los Angeles, California,","place":["United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2725-5367","authenticated-orcid":false,"given":"Samayan","family":"Bhattacharya","sequence":"additional","affiliation":[{"name":"Department of Biomedical Engineering, Corwin D. 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