ArticleJournal of Alzheimer's disease : JAD2017
Development of a Late-Life Dementia Prediction Index with Supervised Machine Learning in the Population-Based CAIDE Study.
Article in Journal of Alzheimer's disease : JAD, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled it.
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Who cites it
16 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Multi-domain prognostic models used in middle-aged adults without known cognitive impairment for predicting subsequent dementia.The Cochrane database of systematic reviews · 2023Pooled it
- Predicting overactive bladder from inflammatory markers: A machine learning approach using NHANES 2005-2020.Biomolecules & biomedicine · 2025Article
- Bias in machine learning applications to address non-communicable diseases at a population-level: a scoping review.BMC public health · 2024Article
- Artificial intelligence for dementia prevention.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2023Review
- Diagnostic Efficacy and Clinical Relevance of Artificial Intelligence in Detecting Cognitive Decline.Cureus · 2023Review
- External validation of existing dementia prediction models on observational health data.BMC medical research methodology · 2022Review
- Artificial Intelligence Models in the Diagnosis of Adult-Onset Dementia Disorders: A Review.Bioengineering (Basel, Switzerland) · 2022Review
- A Risk Prediction Model Based on Machine Learning for Cognitive Impairment Among Chinese Community-Dwelling Elderly People With Normal Cognition: Development and Validation Study.Journal of medical Internet research · 2021Article
- Article
- Using Dual Neural Network Architecture to Detect the Risk of Dementia With Community Health Data: Algorithm Development and Validation Study.JMIR medical informatics · 2020Article
- Article
- Applications of Machine Learning Predictive Models in the Chronic Disease Diagnosis.Journal of personalized medicine · 2020Review
- Secular changes in dementia risk indices among 70-year-olds: a comparison of two Finnish cohorts born 20 years apart.Aging clinical and experimental research · 2020Article
- Prediction models for dementia and neuropathology in the oldest old: the Vantaa 85+ cohort study.Alzheimer's research & therapy · 2019Article
- Predicting Global Cognitive Decline in the General Population Using the Disease State Index.Frontiers in aging neuroscience · 2019Article
- Machine Learning in Acute Ischemic Stroke Neuroimaging.Frontiers in neurology · 2018Review
Corrections and comments
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
BACKGROUND AND
objectiveThis study aimed to develop a late-life dementia prediction model using a novel validated supervised machine learning method, the Disease State Index (DSI), in the Finnish population-based CAIDE study.
methodsThe CAIDE study was based on previous population-based midlife surveys. CAIDE participants were re-examined twice in late-life, and the first late-life re-examination was used as baseline for the present study. The main study population included 709 cognitively normal subjects at first re-examination who returned to the second re-examination up to 10 years later (incident dementia n = 39). An extended population (n = 1009, incident dementia 151) included non-participants/non-survivors (national registers data). DSI was used to develop a dementia index based on first re-examination assessments. Performance in predicting dementia was assessed as area under the ROC curve (AUC).
resultsAUCs for DSI were 0.79 and 0.75 for main and extended populations. Included predictors were cognition, vascular factors, age, subjective memory complaints, and APOE genotype.
conclusionThe supervised machine learning method performed well in identifying comprehensive profiles for predicting dementia development up to 10 years later. DSI could thus be useful for identifying individuals who are most at risk and may benefit from dementia prevention interventions.
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