Estimating 10-Year Cardiovascular Disease Risk in Primary Prevention Using UK Electronic Health Records and a Hybrid Multitask BERT Model: Retrospective Cohort Study.

ABSTRACT:

Background

Cardiovascular disease (CVD) remains a leading cause of preventable morbidity and mortality, highlighting the need for early risk stratification in primary prevention. Traditional Cox models assume proportional hazards and linear effects, limiting flexibility. While machine learning offers greater expressiveness, many models rely solely on structured data and overlook time-to-event (TTE) information. Integrating structured and textual representations may enhance prediction and support equitable assessment across clinical subgroups.

Objective

This study aims to develop a hybrid multitask deep learning model (MT-BERT [multitask Bidirectional Encoder Representations from Transformers]) integrating structured and textual features from electronic health records (EHRs)

SUBMITTER: Liu T 

PROVIDER: S-EPMC12620595 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature