Leveraging electronic health records and knowledge networks for Alzheimer's disease prediction and sex-specific biological insights.

ABSTRACT: Identification of Alzheimer's disease (AD) onset risk can facilitate interventions before irreversible disease progression. We demonstrate that electronic health records from the University of California, San Francisco, followed by knowledge networks (for example, SPOKE) allow for (1) prediction of AD onset and (2) prioritization of biological hypotheses, and (3) contextualization of sex dimorphism. We trained random forest models and predicted AD onset on a cohort of 749 individuals with AD and 250,545 controls with a mean area under the receiver operating characteristic of 0.72 (7 years prior) to 0.81 (1 day prior). We further harnessed matched cohort models to identify conditions with predictive power before AD onset. Knowledge networks highlight shared genes between multiple top predic

SUBMITTER: Tang AS 

PROVIDER: S-EPMC10950787 | biostudies-literature | 2024 Mar

REPOSITORIES: biostudies-literature