Early prediction of Alzheimer's disease and related dementias using real‐world electronic health records
Qian Li, Xi Yang, Jie Xu, Yi Guo, Xing He, Hui Hu, Tianchen Lyu, David E. Marra, Amber Miller, Glenn E. Smith, Steven T. DeKosky, Richard D. Boyce, Karen C. Schliep, Elizabeth Shenkman, Demetrius M. Maraganore, Yonghui Wu, Jiang Bian
Abstract
INTRODUCTION: This study aims to explore machine learning (ML) methods for early prediction of Alzheimer's disease (AD) and related dementias (ADRD) using the real-world electronic health records (EHRs). METHODS: A total of 23,835 ADRD and 1,038,643 control patients were identified from the OneFlorida+ Research Consortium. Two ML methods were used to develop the prediction models. Both knowledge-driven and data-driven approaches were explored. Four computable phenotyping algorithms were tested. RESULTS: The gradient boosting tree (GBT) models trained with the data-driven approach achieved the best area under the curve (AUC) scores of 0.939, 0.906, 0.884, and 0.854 for early prediction of ADRD 0, 1, 3, or 5 years before diagnosis, respectively. A number of important clinical and sociodemographic factors were identified. DISCUSSION: We tested various settings and showed the predictive ability of using ML approaches for early prediction of ADRD with EHRs. The models can help identify high-risk individuals for early informed preventive or prognostic clinical decisions.