ArticleAlzheimer's & dementia (Amsterdam, Netherlands)
Develop and validate a computable phenotype for the identification of Alzheimer's disease patients using electronic health record data.
Article in Alzheimer's & dementia (Amsterdam, Netherlands). The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Observational
- CMC-ID: identifying children and youth with medical complexity using an electronic health record.BMJ health & care informatics · 2026Article
- Using large language models to enhance clinically-driven missing data recovery algorithms in electronic health records.JAMIA open · 2026Article
- Leveraging electronic health records to examine differential clinical outcomes in people with Alzheimer's disease.Communications medicine · 2026Article
- Early prediction of Alzheimer's disease using longitudinal electronic health records of US military veterans.Communications medicine · 2026Article
- 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
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21 authors.
Funding
Abstract
introductionAlzheimer's disease (AD) is often misclassified in electronic health records (EHRs) when relying solely on diagnosis codes. This study aimed to develop a more accurate, computable phenotype (CP) for identifying AD patients using structured and unstructured EHR data.
methodsWe used EHRs from the University of Florida Health (UFHealth) system and created rule-based CPs iteratively through manual chart reviews. The CPs were then validated using data from the University of Texas Health Science Center at Houston (UTHealth) and the University of Minnesota (UMN).
resultsOur best-performing CP was " DISCUSSION: We developed and validated rule-based CPs for AD identification with good performance, which will be crucial for studies that aim to use real-world data like EHRs. Highlights: Developed a computable phenotype (CP) to identify Alzheimer's disease (AD) patients using EHR data.Utilized both structured and unstructured EHR data to enhance CP accuracy.Achieved a high F1-score of 0.817 at UFHealth, and 0.961 and 0.623 at UTHealth and UMN.Validated the CP across different demographics, ensuring robustness and fairness.
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