A Bayesian Network Model of Pregnancy Outcomes for England and Wales

TY - JOUR

T1 - A Bayesian Network Model of Pregnancy Outcomes for England and Wales

AU - McLachlan, Scott

AU - Rose, Louise

AU - Grosan, Crina

AU - Daley, Bridget

AU - Saidi, Samir

AU - Kyrimi, Evangelia

AU - Dube, Kudakwashe

AU - Neil, Martin

AU - Fenton, Norman

N1 - Publisher Copyright: © 2025

PY - 2025/3/15

Y1 - 2025/3/15

N2 - 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 expertelicitation 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 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.

AB - 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 expertelicitation 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 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.

UR - http://www.scopus.com/inward/record.url?scp=86000774082&partnerID=8YFLogxK

U2 - 10.1016/j.compbiomed.2025.110026

DO - 10.1016/j.compbiomed.2025.110026

M3 - Article

SN - 0010-4825

VL - 189

JO - Computers in Biology and Medicine

JF - Computers in Biology and Medicine

IS - 110026

M1 - 110026

ER -