Development and Validation of a Bayesian Network-Based Model for Predicting Coronary Heart Disease Risk From Electronic Health Records.

ABSTRACT:

Background

Traditional risk evaluation models have been applied to guide public health and clinical practice in various studies. However, the application of existing methods to data sets with missing and censored data, as is often the case in electronic health records, requires additional considerations. We aimed to develop and validate a predictive model that exhibits high performance with data sets that contain missing and censored data.

Methods and results

This is a retrospective cohort study of coronary heart disease at Weihai Municipal Hospital on unique patients aged 18 to 96 years between 2013 and 2021. A total of 169 692 participants formed our study population, of which 10 895 participants were diagnosed with coronary heart disease. Models were built for the risk of

SUBMITTER: Suo X 

PROVIDER: S-EPMC10863831 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature