biostudies-literatureRao SNCI NIH HHS100873https://www.ebi.ac.uk/biostudies/studies/S-EPMC12935155biostudies-literatureUnknown7(6)<h4>Background</h4>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.<h4>Methods</h4>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 generThe Lancet. Digital healthRefined selection of individuals for preventive cardiovascular disease treatment with a transformer-based risk model.PMC12935155P30 CA008748Vickers ASalimi-Khorshidi GRao SLi YYau CWamil MJackson RMamouei MRahimi KDanaei GNazarzadeh MCollins GSfalseRefined selection of individuals for preventive cardiovascular disease treatment with a transformer-based risk model.<h4>Background</h4>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.<h4>Methods</h4>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 gener2025-01-01T00:00:00Z2025 Jun2026-07-16T20:44:24.948Z2026-07-10T03:11:16.03ZS-EPMC129351554046134910.1016/j.landig.2025.03.005