Abstract
ABSTRACTINTRODUCTIONAlzheimer’s Disease (AD) are often misclassified in electronic health records (EHRs) when relying solely on diagnostic codes. This study aims to develop a more accurate, computable phenotype (CP) for identifying AD patients by using both structured and unstructured EHR data.METHODSWe used EHRs from the University of Florida Health (UF Health) 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 (UT Health) and the University of Minnesota (UMN).RESULTSOur best-performing CP is “patient has at least 2 AD diagnoses and AD-related keywords” with an F1-score of 0.817 at UF, and 0.961 and 0.623 at UT Health and UMN, respectively.DISCUSSIONWe developed and validated rule-based CPs for AD identification with good performance, crucial for studies that aim to use real-world data like EHRs.
Publisher
Cold Spring Harbor Laboratory
Reference32 articles.
1. Population estimate of people with clinical Alzheimer's disease and mild cognitive impairment in the United States (2020–2060)
2. Estimating the global mortality from Alzheimer’s disease and other dementias: A new method and results from the Global Burden of Disease study 2019
3. 2023 Alzheimer's disease facts and figures
4. NAPA - National Alzheimer’s Project Act. ASPE. Accessed March 25, 2022. https://aspe.hhs.gov/collaborations-committees-advisory-groups/napa
5. of the Commissioner O. Real-World Evidence. Published April 7, 2020. Accessed May 7, 2020. https://www.fda.gov/science-research/science-and-research-special-topics/real-world-evidence