Develop and validate a computable phenotype for the identification of Alzheimer's disease patients using electronic health record data.

ABSTRACT:

Introduction

Alzheimer'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.

Methods

We 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).

Results

Our best-performing CP was "patient has at least 2 AD diagnoses and AD-related keywords in AD encounters," with an F1-score of 0.817 at UF, 0.961 at UTHealth, and 0.623 at UMN, respectiv

SUBMITTER: He X 

PROVIDER: S-EPMC11220631 | biostudies-literature | 2024 Jul-Sep

REPOSITORIES: biostudies-literature