{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,13]],"date-time":"2026-10-13T21:59:38Z","timestamp":1791928778437,"version":"4.3.4"},"reference-count":27,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2021,12,31]],"date-time":"2021-12-31T00:00:00Z","timestamp":1640908800000},"content-version":"vor","delay-in-days":364,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"content-domain":{"domain":["alz-journals.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["A&amp;D Transl Res &amp; Clin Interv"],"published-print":{"date-parts":[[2021,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Introduction<\/jats:title>\n                    <jats:p>Many research studies have well investigated Alzheimer's disease (AD) detection and progression. However, the continuous\u2010time survival prediction of AD is not yet fully explored to support medical practitioners with predictive analytics. In this study, we develop a survival analysis approach to examine interactions between patients\u2019 inherent temporal and medical patterns and predict the probability of the AD next stage progression during a time period. The likelihood of reaching the following AD stage is unique to a patient, helping the medical practitioner analyze the patient's condition and provide personalized treatment recommendations ahead of time.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methodologies<\/jats:title>\n                    <jats:p>We simulate the disease progression based on patient profiles using non\u2010linear survival methods\u2014non\u2010linear Cox proportional hazard model (Cox\u2010PH) and neural multi\u2010task logistic regression (N\u2010MTLR). In addition, we evaluate the concordance index (C\u2010index) and Integrated Brier Score (IBS) to describe the evolution to the next stage of AD. For personalized forecasting of disease, we also developed deep neural network models using the dataset provided by the National Alzheimer's Coordinating Center with their multiple\u2010visit details between 2005 and 2017.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The experiment results show that our N\u2010MTLR based survival models outperform the CoxPH models, the best of which gives Concordance\u2010Index of 0.79 and IBS of 0.09. We obtained 50 critical features out of 92 by applying recursive feature elimination and random forest techniques on the clinical data; the top ones include normal cognition and behavior, criteria for dementia, community affairs, etc. Our study demonstrates that selecting critical features can improve the effectiveness of probabilities at each time interval.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>The proposed deep learning\u2010based survival method and model can be used by medical practitioners to predict the patients\u2019 AD shift efficiently and recommend personalized treatment to mitigate or postpone the effects of AD. More generally, our proposed survival analysis approach for predicting disease stage shift can be used for other progressive diseases such as cancer, Huntington's disease, and scleroderma, just to mention a few, using the corresponding clinical data.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/trc2.12229","type":"journal-article","created":{"date-parts":[[2021,12,31]],"date-time":"2021-12-31T06:42:28Z","timestamp":1640932948000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["Time\u2010to\u2010event prediction using survival analysis methods for Alzheimer's disease progression"],"prefix":"10.1002","volume":"7","author":[{"given":"Rahul","family":"Sharma","sequence":"first","affiliation":[{"name":"The Pennsylvania State University  Malvern Pennsylvania USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Harsh","family":"Anand","sequence":"additional","affiliation":[{"name":"The Pennsylvania State University  Malvern Pennsylvania USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8976-7894","authenticated-orcid":false,"given":"Youakim","family":"Badr","sequence":"additional","affiliation":[{"name":"The Pennsylvania State University  Malvern Pennsylvania USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Robin G.","family":"Qiu","sequence":"additional","affiliation":[{"name":"The Pennsylvania State University  Malvern Pennsylvania USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2021,12,31]]},"reference":[{"key":"e_1_2_10_2_1","unstructured":"NaqviE.Alzheimer's disease statistics. 2017;https:\/\/alzheimersnewstoday.com\/alzheimers\u2010disease\u2010statistics(accessed December 10 2021)"},{"key":"e_1_2_10_3_1","doi-asserted-by":"publisher","DOI":"10.1056\/NEJMp1500523"},{"key":"e_1_2_10_4_1","doi-asserted-by":"publisher","DOI":"10.1038\/srep26094"},{"key":"e_1_2_10_5_1","first-page":"286","volume-title":"Generating Multi\u2010Label Discrete Patient Records Using Generative Adversarial Networks","author":"Choi E","year":"2017"},{"key":"e_1_2_10_6_1","doi-asserted-by":"publisher","DOI":"10.1093\/jamia\/ocw042"},{"key":"e_1_2_10_7_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-16399-0_23"},{"key":"e_1_2_10_8_1","doi-asserted-by":"publisher","DOI":"10.4236\/abcr.2017.61001"},{"key":"e_1_2_10_9_1","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0181001"},{"key":"e_1_2_10_10_1","doi-asserted-by":"publisher","DOI":"10.1186\/s12879-019-4544-8"},{"key":"e_1_2_10_11_1","doi-asserted-by":"publisher","DOI":"10.1016\/S1474-4422(06)70537-3"},{"key":"e_1_2_10_12_1","doi-asserted-by":"publisher","DOI":"10.1038\/nrneurol.2010.54"},{"key":"e_1_2_10_13_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.1365-2796.2004.01386.x"},{"key":"e_1_2_10_14_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-018-27337-w"},{"key":"e_1_2_10_15_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41746-020-0256-0"},{"key":"e_1_2_10_16_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jalz.2010.03.018"},{"key":"e_1_2_10_17_1","doi-asserted-by":"publisher","DOI":"10.1080\/01621459.1958.10501452"},{"key":"e_1_2_10_18_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4612-4380-9_37"},{"key":"e_1_2_10_19_1","doi-asserted-by":"publisher","DOI":"10.4082\/kjfm.18.0114"},{"issue":"3","key":"e_1_2_10_20_1","first-page":"6","article-title":"Cox models survival analysis based on breast cancer treatments","volume":"7","author":"Abadi A","year":"2014","journal-title":"Iran J Cancer Prev"},{"key":"e_1_2_10_21_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.math.2013.08.007"},{"key":"e_1_2_10_22_1","doi-asserted-by":"publisher","DOI":"10.3889\/oamjms.2019.109"},{"key":"e_1_2_10_23_1","doi-asserted-by":"publisher","DOI":"10.1186\/s12874-018-0482-1"},{"key":"e_1_2_10_24_1","doi-asserted-by":"publisher","DOI":"10.1001\/jama.1982.03320430047030"},{"key":"e_1_2_10_25_1","doi-asserted-by":"publisher","DOI":"10.1002\/bimj.200610301"},{"key":"e_1_2_10_26_1","article-title":"Deep neural networks for survival analysis based on a multi\u2010task framework","author":"Fotso S","year":"2018","journal-title":"arXiv preprint arXiv"},{"key":"e_1_2_10_27_1","article-title":"A disciplined approach to neural network hyper\u2010parameters: part 1\u2014learning rate, batch size, momentum, and weight decay","author":"Smith LN","year":"2018","journal-title":"ArXiv"},{"key":"e_1_2_10_28_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2016.07.018"}],"container-title":["Alzheimer's &amp; 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