ArticleEClinicalMedicine2022
Development of a novel dementia risk prediction model in the general population: A large, longitudinal, population-based machine-learning study.
Article in EClinicalMedicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 60 papers, 2 of them syntheses that pooled it.
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Who cites it
60 citing papers in PubMed, 2 syntheses or guidelines pooled it, 94 citations in OpenAlex.
- Multimodal AI for Alzheimer Disease Diagnosis: Systematic Review of Datasets, Models, and Modalities.Journal of medical Internet research · 2026Pooled it
- Cardiovascular disease risk models and dementia or cognitive decline: a systematic review.Frontiers in aging neuroscience · 2023Pooled it
- Baseline predictors of neurophysiological response to combined physical and cognitive training in older adults with subjective cognitive decline.BMC geriatrics · 2026Trial
- Plasma proteomics framework predicts metabolic dysfunction-associated steatotic liver disease up to 16 years before onset.Nature aging · 2026Article
- Alzheimer's disease risk prediction from clinical and social determinants of health: a machine learning cohort study in UK Biobank.BMJ health & care informatics · 2026Article
- Development and validation of a Cog-Free risk predicting tool for dementia in a community setting.General psychiatry · 2026Article
- Prediction of Depression in Women With Metabolic Dysfunction-Associated Fatty Liver Disease Using Routine Blood Tests: A Five-Year Longitudinal Analysis From the UK Biobank.Alpha psychiatry · 2026Article
- Associations of Dietary Antioxidant and Phytochemical Indices with Cognitive Function: Mediating Roles of Basal Metabolic Rate and Systemic Inflammation.Antioxidants (Basel, Switzerland) · 2026Article
- How does type 2 diabetes modify the risk of Alzheimer's disease?Alzheimer's & dementia : the journal of the Alzheimer's Association · 2026Review
- Integrative machine learning approach to risk prediction for dementia and Alzheimer's disease.GeroScience · 2026Article
- Sensitivity comparison of longitudinal cognitive function indicators of Alzheimer's disease after mild cognitive impairment: a prospective cohort study.Scientific reports · 2026Article
- The CAIDE dementia risk score indicates elevated cognitive risk in late adulthood: a structural and functional neuroimaging study.GeroScience · 2026Article
- Early prediction of Alzheimer's disease using longitudinal electronic health records of US military veterans.Communications medicine · 2026Article
- Fostering equity in precision health through diverse 3D facial data.Frontiers in medical technology · 2026Article
- Resources and applications of public biomedical data.Frontiers in bioinformatics · 2026Review
- Real-world prediction of early-onset dementia by health record data: A multi-center machine learning study.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2025Article
- Development and multi-center validation of a high-performance predictive model for early detection of cognitive impairment in older adults: data-based on communities in Northern China.Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology · 2025Article
- Outdoor physical activity, residential green spaces and the risk of dementia in the UK Biobank cohort.Communications medicine · 2025Article
- Identification and validation of an explainable machine learning model for vascular depression diagnosis in the older adults: a multicenter cohort study.BMC medicine · 2025Article
- Medical laboratory data-based models: opportunities, obstacles, and solutions.Journal of translational medicine · 2025Review
Corrections and comments
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Authors and funding
7 authors at 2 institutions in 1 country.
Funding
Abstract
Background: The existing dementia risk models are limited to known risk factors and traditional statistical methods. We aimed to employ machine learning (ML) to develop a novel dementia prediction model by leveraging a rich-phenotypic variable space of 366 features covering multiple domains of health-related data. Methods: In this longitudinal population-based cohort of the UK Biobank (UKB), 425,159 non-demented participants were enrolled from 22 recruitment centres across the UK between March 1, 2006 and October 31, 2010. We implemented a data-driven strategy to identify predictors from 366 candidate variables covering a comprehensive range of genetic and environmental factors and developed the ML model to predict incident dementia and Alzheimer's Disease (AD) within five, ten, and much longer years (median 11.9 [Interquartile range 11.2-12.5] years). Findings: During a follow-up of 5,023,337 person-years, 5287 and 2416 participants developed dementia and AD, respectively. A novel UKB dementia risk prediction (UKB-DRP) model comprising ten predictors including age, Interpretation: Our ML-based UKB-DRP model incorporated ten easily accessible predictors with solid predictive power for incident dementia and AD within five, ten, and much longer years, which can be used to identify individuals at high risk of dementia and AD in the general population. Funding: This study was funded by grants from the Science and Technology Innovation 2030 Major Projects (2022ZD0211600), National Key R&D Program of China (2018YFC1312904, 2019YFA070950), National Natural Science Foundation of China (282071201, 81971032, 82071997), Shanghai Municipal Science and Technology Major Project (2018SHZDZX01), Research Start-up Fund of Huashan Hospital (2022QD002), Excellence 2025 Talent Cultivation Program at Fudan University (3030277001), Shanghai Rising-Star Program (21QA1408700), Medical Engineering Fund of Fudan University (yg2021-013), and the 111 Project (No. B18015).
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.