Validation of risk prediction models applied to longitudinal electronic health record data for the prediction of major cardiovascular events in the presence of data shifts.

ABSTRACT:

Aims

Deep learning has dominated predictive modelling across different fields, but in medicine it has been met with mixed reception. In clinical practice, simple, statistical models and risk scores continue to inform cardiovascular disease risk predictions. This is due in part to the knowledge gap about how deep learning models perform in practice when they are subject to dynamic data shifts; a key criterion that common internal validation procedures do not address. We evaluated the performance of a novel deep learning model, BEHRT, under data shifts and compared it with several ML-based and established risk models.

Methods and results

Using linked electronic health records of 1.1 million patients across England aged at least 35 years between 1985 and 2015, we replicated thr

SUBMITTER: Li Y 

PROVIDER: S-EPMC9779795 | biostudies-literature | 2022 Dec

REPOSITORIES: biostudies-literature