Refined selection of individuals for preventive cardiovascular disease treatment with a transformer-based risk model.

ABSTRACT:

Background

Although statistical models have been commonly used to identify patients at risk of cardiovascular disease for preventive therapy, these models tend to over-recommend therapy. Moreover, in populations with pre-existing diseases, the current approach is to indiscriminately treat all, as modelling in this context is currently inadequate. This study aimed to develop and validate the Transformer-based Risk assessment survival (TRisk) model, a novel deep learning model, for predicting 10-year risk of cardiovascular disease in both the primary prevention population and individuals with diabetes.

Methods

An open cohort of 3 million adults aged 25-84 years was identified using linked electronic health records from 291 general practices, for model development, and 98 gener

SUBMITTER: Rao S 

PROVIDER: S-EPMC12935155 | biostudies-literature | 2025 Jun

REPOSITORIES: biostudies-literature