Cohort discovery and risk stratification for Alzheimer's disease: an electronic health record‐based approach

Abstract

Abstract Background We 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). Method Using 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. Results Applied 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. Conclusion Routinely 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.

Keywords

• cohort discovery
• early prediction
• electronic health record
• machine learning