Early prediction of Alzheimer's disease and related dementias using real‐world electronic health records

Author:

Li Qian1,Yang Xi1,Xu Jie1ORCID,Guo Yi1,He Xing1,Hu Hui2,Lyu Tianchen1,Marra David3,Miller Amber4,Smith Glenn5,DeKosky Steven4,Boyce Richard D.6,Schliep Karen7,Shenkman Elizabeth1,Maraganore Demetrius8,Wu Yonghui1,Bian Jiang1ORCID

Affiliation:

1. Department of Health Outcomes and Biomedical Informatics College of Medicine University of Florida Gainesville Florida USA

2. Channing Division of Network Medicine Brigham and Women's Hospital and Harvard Medical School Boston Massachusetts USA

3. Department of Psychology VA Boston Healthcare System Boston Massachusetts USA

4. Department of Neurology College of Medicine University of Florida Gainesville Florida USA

5. Department of Clinical and Health Psychology University of Florida Gainesville Florida USA

6. Department of Biomedical Informatics University of Pittsburgh Pittsburgh Pennsylvania USA

7. Department of Family and Preventive Medicine University of Utah Salt Lake City Utah USA

8. Department of Neurology School of Medicine Tulane University New Orleans Louisiana USA

Abstract

AbstractIntroductionThis 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).MethodsA 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.ResultsThe 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.DiscussionWe 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.

Funder

National Institute on Aging

National Cancer Institute

Centers for Disease Control and Prevention

Publisher

Wiley

Subject

Psychiatry and Mental health,Cellular and Molecular Neuroscience,Geriatrics and Gerontology,Neurology (clinical),Developmental Neuroscience,Health Policy,Epidemiology

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