Approach and Method for Bayesian Network Modelling: A Case Study in Pregnancy Outcomes for England and Wales

Efforts to fully exploit the rich potential of Bayesian Networks (BNs) have hitherto not seen a practical approach for development of domain-specific models using large-scale public statistics which have the potential to reduce the time required to develop probability tables and train the model. As a result, the duration of projects seeking to develop health BNs tend to be measured in years due to their reliance on obtaining ethics approval and collecting, normalising, and discretising collections of patient EHRs. This work addresses this challenge by investigating a new approach to developing health BNs that combines expert elicitation with knowledge from literature and national health statistics. The approach presented here is evaluated through the development of a BN for pregnancy complications and outcomes using national health statistics for all births in England and Wales during 2021. The result is a BN that when validated using vignettes against other common types of predictive models including multivariable logistic regression and nomograms produces comparable predictions. The BN using our approach and large-scale public statistics was also developed in a project with a duration measured in months rather than years. The unique contributions of this paper are a new efficient approach to BN development and a working BN capable of reasoning over a broad range of pregnancy-related conditions and outcomes.