Longitudinal multi-modal data prediction model for mild cognitive impairment by deep survival analysis
Year: 2026
Type: article
Abstract: BACKGROUND: Timely prediction of cognitive decline in patients with Mild Cognitive Impairment (MCI) is crucial for guiding optimal therapeutic interventions. In this study, we aimed to develop a deep survival analysis model that leverages longitudinal, multi-modal data to estimate the probability of dementia conversion, thereby facilitating personalized treatment planning in clinical practice. METHODS: We employed a deep neural network model specifically designed for survival analysis to predict the progression from MCI to Alzheimer’s Disease (AD). The model integrated longitudinal biomarkers, including neuropsychological assessments and neuroimaging measures, along with baseline demographic characteristics and genetic risk factors, using data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. RESULTS: This study enrolled 922 baseline MCI patients for analysis. The predictive performance was evaluated using a test set at time intervals [Formula: see text] = 1, 2, 3, 4 years from the landmark time s = 1. The prognostic model exhibited outstanding predictive capability, attaining cdAUC values of 0.9089 ± 0.01 alongside BS of 0.1651 ± 0.01 with [Formula: see text] = 1 year 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: By systematically integrating diverse longitudinal biomarkers, we developed a dynamic prediction model for MCI using deep survival analysis. This approach enables accurate individual risk stratification, facilitates the early identification of high-risk individuals, and supports informed, personalized clinical decision-making.
Institution: Shanxi Medical University
Language: English
Subfield: Psychiatry and Mental health
Field: Medicine
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $2,990
Funders: U.S. Department of Defense, Alzheimer's Association, Bristol-Myers Squibb, Eli Lilly and Company, Pfizer, Biogen, University of Southern California, Meso Scale Diagnostics, BioClinica, Novartis Pharmaceuticals Corporation, Northern California Institute for Research and Education, National Natural Science Foundation of China, Eisai, Natural Science Foundation of Shanxi Province, Ministry of Education, India, Shanxi Medical University, Servier, H. Lundbeck A/S, IXICO, National Institutes of Health, Genentech, Canadian Institutes of Health Research
Awards: G1298807586, G1554142629, G1593117279, G1650819308, G1880935189, Alzheimer's Disease Neuroimaging Initiative, G2100670906, G2152643862, G2189010064, G2287065097, G2340207221, G2471646051, G2522063444, G3131374986, G3553156159, G3710456697, G3752451908, G3870774570, G3908963977, G4119534169, G43180419, G4475667458, G4501223628, G470063782, G4800177743, G4900191342, G5207404921, G5209990037, G528601746, G5365730499, EFFECTS OF TRAUMATIC BRAIN INJURY (TBI) AND POST TRAUMATIC STRESS DISORDER (PTSD) ON ALZHEIMER'S DISEASE (AD) IN VETERANS USING IMAGING AND BIOMARKERS IN THE AD NEUROIMAGING INITIATIVE (ADNI)", G5654639750, G5740349702, G5850682332, G613619302, G6173816828, G6210868510, G6371410416, G6412110892, G6447063303, G6591097260, G6719828067, G6792506723, G6899163935, G72415811, G7298697282, G764699806, G7799614282, G781725486, G8151151163, G8228018387, G8281378126, G839208290, G8852128998, G8895907669, G8897063270, G8957659042, G9000039018, G9000122335
OpenAlex ID: W7129057981