ArticleAlzheimer's & dementia (New York, N. Y.)2020
Cohort discovery and risk stratification for Alzheimer's disease: an electronic health record-based approach.
Article in Alzheimer's & dementia (New York, N. Y.), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 1 of them a synthesis that pooled it.
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Who cites it
20 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Electronic health record-based prediction models for dementia detection: a systematic review of model performance and quality.Journal of the American Medical Informatics Association : JAMIA · 2026Pooled it
- Leveraging electronic health records for atrial fibrillation cohort generation.Health information science and systems · 2026Article
- Leveraging electronic health records to examine differential clinical outcomes in people with Alzheimer's disease.Communications medicine · 2026Article
- A neuroimaging biomarker for disease staging in clinically diagnosed Alzheimer's disease.BMC medicine · 2026Article
- Early prediction of Alzheimer's disease using longitudinal electronic health records of US military veterans.Communications medicine · 2026Article
- CARE-AD: a multi-agent large language model framework for Alzheimer's disease prediction using longitudinal clinical notes.NPJ digital medicine · 2025Article
- Real-world health care costs and resource utilization associated with mild cognitive impairment in the United States: A retrospective cohort study of commercial and Medicare data.Journal of managed care & specialty pharmacy · 2025Article
- Article
- Prediction of conversion from mild cognitive impairment to Alzheimer's disease and simultaneous feature selection and grouping using Medicaid claim data.Alzheimer's research & therapy · 2024Article
- The age-specific comorbidity burden of mild cognitive impairment: a US claims database study.Alzheimer's research & therapy · 2023Article
- Use of blood pressure measurements extracted from the electronic health record in predicting Alzheimer's disease: A retrospective cohort study at two medical centers.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2022Article
- The medicinal chemistry of mitochondrial dysfunction: a critical overview of efforts to modulate mitochondrial health.RSC medicinal chemistry · 2021Review
- Article
- Cohort discovery and risk stratification for Alzheimer's disease: an electronic health record-based approach.Alzheimer's & dementia (New York, N. Y.) · 2020Article
- Estimated Effects of Comorbidities on Risk of All-cause Dementia in Patients with Mild Cognitive Impairment.Sage open agingArticle
- Risk prediction of mild cognitive impairment using electronic health record data.Alzheimer's & dementia (Amsterdam, Netherlands)Article
- Clinical factors predicting the rate of cognitive decline in a US memory clinic: An electronic health record study.Alzheimer's & dementia (New York, N. Y.)Article
- What's New in Dementia Risk Prediction Modelling? An Updated Systematic Review.Dementia and geriatric cognitive disorders extraReview
- Predicting 5-year dementia conversion in veterans with mild cognitive impairment.Alzheimer's & dementia (Amsterdam, Netherlands)Article
- Develop and validate a computable phenotype for the identification of Alzheimer's disease patients using electronic health record data.Alzheimer's & dementia (Amsterdam, Netherlands)Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
backgroundWe sought to leverage data routinely collected in electronic health records (EHRs), with the goal of developing patient risk stratification tools for predicting risk of developing Alzheimer's disease (AD).
methodUsing EHR data from the University of Michigan (UM) hospitals and consensus-based diagnoses from the Michigan Alzheimer's Disease Research Center, we developed and validated a cohort discovery tool for identifying patients with AD. Applied to all UM patients, these labels were used to train an EHR-based machine learning model for predicting AD onset within 10 years.
resultsApplied to a test cohort of 1697 UM patients, the model achieved an area under the receiver operating characteristics curve of 0.70 (95% confidence interval = 0.63-0.77). Important predictive factors included cardiovascular factors and laboratory blood testing.
conclusionRoutinely collected EHR data can be used to predict AD onset with modest accuracy. Mining routinely collected data could shed light on early indicators of AD appearance and progression.
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