Authors:
Scott McLachlan
1
;
Bridget Daley
2
;
Sam Saidi
3
;
Evangelia Kyrimi
4
;
Kudakwashe Dube
1
;
Crina Grossan
1
;
Martin Neil
4
;
Louise Rose
1
and
Norman Fenton
4
Affiliations:
1
Nursing, Midwifery and Palliative Care, Kings College London, London, U.K.
;
2
Maternity Unit, Liverpool Women’s Hospital NHS Trust, Liverpool, U.K.
;
3
School of Medicine, University of Sydney, Sydney, Australia
;
4
Electronic Engineering and Computer Science, Queen Mary University of London, London, U.K.
Keyword(s):
Clinical Decision-Support Systems, Bayesian Networks, Predictive Models, Pregnancy Outcomes.
Abstract:
For predicting and reasoning about outcomes of specific medical condition Bayesian Networks (BNs) can provide significant benefits over traditional statistical prediction models. However, developing appropriate and accurate BNs that incorporate key causal aspects of the condition is challenging and time-consuming. This work introduces a novel development approach, merging expert elicitation, literature knowledge, and national health statistics that enables such BNs to be developed efficiently. The approach is applied to build a BN for pregnancy complications and outcomes in England and Wales using 2021 data. The BN showed comparable predictive performance against logistic regression and nomograms, but additionally provides powerful support for decision-making and risk assessment across diverse pregnancy-related conditions and outcomes.