ArticleScientific reports2023
Explainable machine learning aggregates polygenic risk scores and electronic health records for Alzheimer's disease prediction.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers, 1 of them a synthesis that pooled it.
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Who cites it
25 citing papers in PubMed, 1 synthesis or guideline pooled it, 40 citations in OpenAlex.
- Machine learning based algorithms for virtual early detection and screening of neurodegenerative and neurocognitive disorders: a systematic-review.Frontiers in neurology · 2024Pooled it
- Artificial Intelligence-Driven Multimodal Prediction of 10-Year Incident Glaucoma Integrating Genetic and Deep Learning-Derived Imaging Features.Ophthalmology science · 2026Article
- Incorporating external risk information with the Cox model under population heterogeneity: applications to trans-ancestry polygenic hazard scores.Journal of the Royal Statistical Society. Series A, (Statistics in Society) · 2026Article
- Integrative machine learning approach to risk prediction for dementia and Alzheimer's disease.GeroScience · 2026Article
- Improved polygenic risk prediction for alzheimer's disease and related dementias using deep learning: age and APOE-stratified analysis.Alzheimer's research & therapy · 2026Article
- Genetic risk in Alzheimer's disease.NPJ systems biology and applications · 2026Review
- Enhancing glaucoma prediction across ancestries: integrating functional annotation into multi-trait polygenic risk scores.Frontiers in genetics · 2026Article
- An explainable machine learning framework for cardiovascular risk prediction using structured health data.Frontiers in artificial intelligence · 2026Article
- Fair positive unlabeled learning for predicting undiagnosed Alzheimer's disease in diverse electronic health records.NPJ digital medicine · 2025Article
- AI-powered model for accurate prediction of MCI-to-AD progression.Acta pharmaceutica Sinica. B · 2025Article
- A Talk with ChatGPT: The Role of Artificial Intelligence in Shaping the Future of Cardiology and Electrophysiology.Journal of personalized medicine · 2025Article
- Multi-trait Polygenic Probability Risk Score Enhances Glaucoma Prediction Across Ancestries.medRxiv : the preprint server for health sciences · 2025Article
- Sex differences in predicting dyslipidemia using polygenic risk score with fatty liver index and fibrotic nonalcoholic steatohepatitis index.Scientific reports · 2025Article
- Clinical Decision Support for Patient Cases with Asymptomatic Carotid Artery Stenosis Using AI Models and Electronic Medical Records.Journal of cardiovascular development and disease · 2025Article
- UK Biobank-A Unique Resource for Discovery and Translation Research on Genetics and Neurologic Disease.Neurology. Genetics · 2025Review
- Recent Advances in the Application of Artificial Intelligence in Alzheimer's Disease.Current Alzheimer research · 2025Review
- Five-year dementia prediction and decision support system based on real-world data.Frontiers in aging neuroscience · 2025Article
- Article
- Assessing polyomic risk to predict Alzheimer's disease using a machine learning model.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2024Article
- Explainable Machine Learning Models for Brain Diseases: Insights from a Systematic Review.Neurology international · 2024Review
Corrections and comments
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Authors and funding
7 authors at 4 institutions in 1 country.
Funding
Abstract
Alzheimer's disease (AD) is the most common late-onset neurodegenerative disorder. Identifying individuals at increased risk of developing AD is important for early intervention. Using data from the Alzheimer Disease Genetics Consortium, we constructed polygenic risk scores (PRSs) for AD and age-at-onset (AAO) of AD for the UK Biobank participants. We then built machine learning (ML) models for predicting development of AD, and explored feature importance among PRSs, conventional risk factors, and ICD-10 codes from electronic health records, a total of > 11,000 features using the UK Biobank dataset. We used eXtreme Gradient Boosting (XGBoost) and SHapley Additive exPlanations (SHAP), which provided superior ML performance as well as aided ML model explanation. For participants age 40 and older, the area under the curve for AD was 0.88. For subjects of age 65 and older (late-onset AD), PRSs were the most important predictors. This is the first observation that PRSs constructed from the AD risk and AAO play more important roles than age in predicting AD. The ML model also identified important predictors from EHR, including urinary tract infection, syncope and collapse, chest pain, disorientation and hypercholesterolemia, for developing AD. Our ML model improved the accuracy of AD risk prediction by efficiently exploring numerous predictors and identified novel feature patterns.
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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.