Predicting cardiovascular risk from national administrative databases using a combined survival analysis and deep learning approach.

ABSTRACT:

Background

Machine learning-based risk prediction models may outperform traditional statistical models in large datasets with many variables, by identifying both novel predictors and the complex interactions between them. This study compared deep learning extensions of survival analysis models with Cox proportional hazards models for predicting cardiovascular disease (CVD) risk in national health administrative datasets.

Methods

Using individual person linkage of administrative datasets, we constructed a cohort of all New Zealanders aged 30-74 who interacted with public health services during 2012. After excluding people with prior CVD, we developed sex-specific deep learning and Cox proportional hazards models to estimate the risk of CVD events within 5 years. Models were c

SUBMITTER: Barbieri S 

PROVIDER: S-EPMC9189958 | biostudies-literature | 2022 Jun

REPOSITORIES: biostudies-literature