How Missing Medication Data Contributes to Bias in Alzheimer’s Disease Machine Learning Models

Abstract

Alzheimer's disease (AD) is the most common cause of dementia, yet many cases go undiagnosed due to limited access to expensive brain scans and lab tests. This study investigated whether medication data could help identify AD. Using data from 1,785 participants in the US-representative National Health and Nutrition Examination Survey 2013– 2014, we identified 105 individuals (5.9%) with memory test scores suggesting possible AD. We evaluated seven machine learning models using medication features. Models that incorporated contextual prescription information, including the reasons for medication use and conditions being treated, achieved the best performance (area under the receiver operating characteristic curve [AUC] 0.61–0.63). In contrast, models using only basic drug names or provider information performed poorly (AUC 0.46–0.51). This performance difference was statistically significant (t = 14.98, p < 0.0001). Our findings suggest that medication data, when analyzed with attention to clinical context, could serve as a low-cost tool for identifying individuals at risk of AD. This approach may help address diagnostic disparities in settings with limited access to advanced testing.