Longitudinal muti-modal data prediction model for mild cognitive impairment by deep survival analysis

Longitudinal muti-modal data prediction model for mild cognitive impairment by deep survival analysis | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Longitudinal muti-modal data prediction model for mild cognitive impairment by deep survival analysis Rong Zhang, Durong Chen, Yao Qin, Hongjuan Han, Hongmei Yu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8233936/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 16 Feb, 2026 Read the published version in BMC Medical Informatics and Decision Making → Version 1 posted 15 You are reading this latest preprint version Abstract Background: Timely prediction of cognitive deterioration patterns in Mild Cognitive Impairment (MCI) patients proves essential for implementing optimal therapeutic interventions. We aimed to develop a deep survival analysis model using longitudinal multi-modal data to predict dementia conversion probabilities, enabling personalized treatment planning in clinical practice. Methods: We used a deep neural network model designed for survival analysis to predict the progression from MCI to Alzheimer’s Disease (AD) using longitudinal biomarkers encompassing neuropsychological assessments and neuroimaging results, along with baseline demographic characteristics and genetic risk indicators from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. Results: This study enrolled 922 baseline MCI patients for analysis. The prognostic model exhibited outstanding predictive capability, attaining cdAUC values of 0.9089±0.01 alongside Brier scores of 0.1651±0.01 at Δt=1 on the test set, when all variable sets were incorporated into the time-dependent Cox survival neural network (tdCoxSNN) model. Through feature significance evaluation, the Functional Activities Questionnaire (FAQ) emerged as the most influential predictive element. Conclusions: Through the systematic integration of various longitudinal biological markers, we developed a dynamic prediction model for MCI using deep survival analysis, enabling effective individual risk stratification and facilitating early identification of high-risk individuals as well as supporting clinical decision-making. Alzheimer’s disease Mild cognitive impairment Deep survival analysis Longitudinal multi-modal data transition probability Full Text Additional Declarations No competing interests reported. Supplementary Files supplementary.docx Cite Share Download PDF Status: Published Journal Publication published 16 Feb, 2026 Read the published version in BMC Medical Informatics and Decision Making → Version 1 posted Editorial decision: Revision requested 18 Dec, 2025 Reviews received at journal 18 Dec, 2025 Reviews received at journal 18 Dec, 2025 Reviews received at journal 17 Dec, 2025 Reviewers agreed at journal 07 Dec, 2025 Reviews received at journal 06 Dec, 2025 Reviewers agreed at journal 05 Dec, 2025 Reviewers agreed at journal 05 Dec, 2025 Reviewers agreed at journal 04 Dec, 2025 Reviewers agreed at journal 04 Dec, 2025 Reviewers invited by journal 04 Dec, 2025 Editor assigned by journal 04 Dec, 2025 Editor invited by journal 04 Dec, 2025 Submission checks completed at journal 03 Dec, 2025 First submitted to journal 03 Dec, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8233936","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":556532682,"identity":"8b5d0cff-2a92-4ad9-90c0-bdc10535b70a","order_by":0,"name":"Rong Zhang","email":"","orcid":"","institution":"Shanxi Medical University","correspondingAuthor":false,"prefix":"","firstName":"Rong","middleName":"","lastName":"Zhang","suffix":""},{"id":556532683,"identity":"34b36bed-ba5a-448a-b965-73fdd10a0a0d","order_by":1,"name":"Durong Chen","email":"","orcid":"","institution":"Shanxi Medical 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We aimed to develop a deep survival analysis model using longitudinal multi-modal data to predict dementia conversion probabilities, enabling personalized treatment planning in clinical practice.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eWe used a deep neural network model designed for survival analysis to predict the progression from MCI to Alzheimer’s Disease (AD) using longitudinal biomarkers encompassing neuropsychological assessments and neuroimaging results, along with baseline demographic characteristics and genetic risk indicators from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eThis study enrolled 922 baseline MCI patients for analysis. 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Through feature significance evaluation, the Functional Activities Questionnaire (FAQ) emerged as the most influential predictive element.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions:\u003c/strong\u003eThrough the systematic integration of various longitudinal biological markers, we developed a dynamic prediction model for MCI using deep survival analysis, enabling effective individual risk stratification and facilitating early identification of high-risk individuals as well as supporting clinical decision-making.\u003c/p\u003e","manuscriptTitle":"Longitudinal muti-modal data prediction model for mild cognitive impairment by deep survival analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-08 22:59:37","doi":"10.21203/rs.3.rs-8233936/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-12-19T04:53:34+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-18T11:50:48+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-18T09:22:05+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-17T14:47:08+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"43974500467872205332506962221240868189","date":"2025-12-07T17:15:56+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-06T11:52:07+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"324484221947563082563282318159767261102","date":"2025-12-05T13:14:22+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"35884889285419539319194448330484530483","date":"2025-12-05T08:17:10+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"287559434179808812979322151298550192890","date":"2025-12-05T03:48:31+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"178777120726820022799555565631160040688","date":"2025-12-05T03:47:26+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-12-05T03:13:01+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-12-05T03:08:41+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-12-04T05:19:32+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-12-03T13:58:56+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Medical Informatics and Decision Making","date":"2025-12-03T13:45:32+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-medical-informatics-and-decision-making","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"midm","sideBox":"Learn more about [BMC Medical Informatics and Decision Making](http://bmcmedinformdecismak.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/midm/default.aspx","title":"BMC Medical Informatics and Decision Making","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"03233cac-1ae3-43be-8195-d81f5e2d64a2","owner":[],"postedDate":"December 8th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-02-23T16:06:39+00:00","versionOfRecord":{"articleIdentity":"rs-8233936","link":"https://doi.org/10.1186/s12911-026-03387-3","journal":{"identity":"bmc-medical-informatics-and-decision-making","isVorOnly":false,"title":"BMC Medical Informatics and Decision Making"},"publishedOn":"2026-02-16 15:56:52","publishedOnDateReadable":"February 16th, 2026"},"versionCreatedAt":"2025-12-08 22:59:37","video":"","vorDoi":"10.1186/s12911-026-03387-3","vorDoiUrl":"https://doi.org/10.1186/s12911-026-03387-3","workflowStages":[]},"version":"v1","identity":"rs-8233936","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8233936","identity":"rs-8233936","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}