Machine learning prediction of survival in centenarians after age 100: a retrospective, population-based cohort study.

ABSTRACT:

Background

Whether survival at extreme ages can be accurately predicted remains unclear. This study explored the feasibility of using machine learning (ML) and electronic health records (EHRs) to predict mortality in centenarians and identify key survival determinants.

Methods

We analyzed 9718 centenarians (83% women) from the population-based EHR database in Hong Kong (2004-2018). Data were randomly split into 70% training and 30% testing cohorts. Using 82 predictors, including demographics, diagnoses, prescriptions, and laboratory results, we trained stepwise logistic regression and four ML algorithms to predict 1-year, 2-year, and 5-year all-cause mortality after age 100. Model performance was evaluated using discrimination (area under the receiver operating characteristi

SUBMITTER: Mak JKL 

PROVIDER: S-EPMC12598932 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature