{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Rao S"],"funding":["NCI NIH HHS"],"pagination":["100873"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC12935155"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["7(6)"],"pubmed_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"],"journal":["The Lancet. Digital health"],"pubmed_title":["Refined selection of individuals for preventive cardiovascular disease treatment with a transformer-based risk model."],"pmcid":["PMC12935155"],"funding_grant_id":["P30 CA008748"],"pubmed_authors":["Vickers A","Salimi-Khorshidi G","Rao S","Li Y","Yau C","Wamil M","Jackson R","Mamouei M","Rahimi K","Danaei G","Nazarzadeh M","Collins GS"],"additional_accession":[]},"is_claimable":false,"name":"Refined selection of individuals for preventive cardiovascular disease treatment with a transformer-based risk model.","description":"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","dates":{"release":"2025-01-01T00:00:00Z","publication":"2025 Jun","modification":"2026-07-16T20:44:24.948Z","creation":"2026-07-10T03:11:16.03Z"},"accession":"S-EPMC12935155","cross_references":{"pubmed":["40461349"],"doi":["10.1016/j.landig.2025.03.005"]}}