Observational studyAlzheimer's & dementia : the journal of the Alzheimer's Association2020
AD risk score for the early phases of disease based on unsupervised machine learning.
Observational study in Alzheimer's & dementia : the journal of the Alzheimer's Association, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed.
- Machine learning prediction of surgical approach and clinical heterogeneity in cervical spondylotic myelopathy.BMC musculoskeletal disorders · 2026Article
- DEVELOPMENT AND APPLICATION OF BRAIN TISSUE BASED MULTI-OMICS PROFILE SCORES FOR ALZHEIMER'S DISEASE.Research square · 2026Article
- Multimodal MRI and machine learning for identifying depression in cerebral small vessel disease: a multicenter study.BMC medical imaging · 2026Article
- Probabilistic clustering using shared latent variable model for assessing Alzheimer's disease biomarkers.Biostatistics (Oxford, England) · 2025Article
- AI-driven prediction of severe respiratory sequelae in COVID-19 patients.Annals of medicine · 2025Article
- BayTetra: A Bayesian Semiparametric Approach for Testing Trajectory Differences.Statistics in medicine · 2025Article
- Article
- Novel methodology for detection and prediction of mild cognitive impairment using resting-state EEG.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2024Article
- Few-shot prediction of amyloid β accumulation from mainly unpaired data on biomarker candidates.NPJ systems biology and applications · 2023Article
- Identification of spinal tuberculosis subphenotypes using routine clinical data: a study based on unsupervised machine learning.Annals of medicine · 2023Article
- The Road to Personalized Medicine in Alzheimer's Disease: The Use of Artificial Intelligence.Biomedicines · 2022Review
- Machine learning-based clustering in cervical spondylotic myelopathy patients to identify heterogeneous clinical characteristics.Frontiers in surgery · 2022Article
- Machine Learning Approach Predicts Probability of Time to Stage-Specific Conversion of Alzheimer's Disease.Journal of Alzheimer's disease : JAD · 2022Article
- Targeted Screening for Alzheimer's Disease Clinical Trials Using Data-Driven Disease Progression Models.Frontiers in artificial intelligence · 2022Article
- Developing the ATX(N) classification for use across the Alzheimer disease continuum.Nature reviews. Neurology · 2021Review
- Artificial intelligence algorithms for the prediction of cognitive decline and dementia: A theoretical-historical narrative review.Journal of Alzheimer's disease reportsReview
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
introductionIdentifying cognitively normal individuals at high risk for progression to symptomatic Alzheimer's disease (AD) is critical for early intervention.
methodsAn AD risk score was derived using unsupervised machine learning. The score was developed using data from 226 cognitively normal individuals and included cerebrospinal fluid, magnetic resonance imaging, and cognitive measures, and validated in an independent cohort.
resultsHigher baseline AD progression risk scores (hazard ratio = 2.70, P < 0.001) were associated with greater risks of progression to clinical symptoms of mild cognitive impairment (MCI). Baseline scores had an area under the curve of 0.83 (95% confidence interval: 0.75 to 0.91) for identifying subjects who progressed to MCI/dementia within 5 years. The validation procedure, using data from the Alzheimer's Disease Neuroimaging Initiative, demonstrated accuracy of prediction across the AD spectrum. DISCUSSION: The derived risk score provides high predictive accuracy for identifying which individuals with normal cognition are likely to show clinical decline due to AD within 5 years.
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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.