Machine learning for sudden cardiac death prediction among older adults using community-based electronic health records.

ABSTRACT:

Background

Machine learning (ML) models have shown good performance in predicting cardiovascular disease risk. However, the usefulness of ML models has yet to be fully elucidated for sudden cardiac death (SCD) risk using long-term follow-up electronic health records (EHRs). This study aimed to develop and validate ML models for SCD risk prediction among community-dwelling older adults using 6-year primary-care EHRs data.

Methods

Data were obtained from the Jinnan Study, an EHR-based cohort of older adults. A total of 30,535 individuals aged ≥ 65 years from three towns were included. The primary outcome was SCD. Candidate predictors included gender, age, heart rate, body mass index (BMI), waist, systolic blood pressure (SBP), diastolic blood pressure (DBP), physical activity,

SUBMITTER: Li Y 

PROVIDER: S-EPMC12534963 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature