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

Scott McLachlan, Bridget Daley, Sam Saidi, Evangelia Kyrimi, Kudakwashe Dube, Crina Grossan, Martin Neil, Louise Rose, Norman Fenton

2024

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.

Paper Citation

in Harvard Style

McLachlan S., Daley B., Saidi S., Kyrimi E., Dube K., Grossan C., Neil M., Rose L. and Fenton N. (2024). Approach and Method for Bayesian Network Modelling: The Case for Pregnancy Outcomes in England and Wales. In Proceedings of the 17th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 2: HEALTHINF; ISBN 978-989-758-688-0, SciTePress, pages 604-612. DOI: 10.5220/0012428600003657

in Bibtex Style

@conference{healthinf24,
author={Scott McLachlan and Bridget Daley and Sam Saidi and Evangelia Kyrimi and Kudakwashe Dube and Crina Grossan and Martin Neil and Louise Rose and Norman Fenton},
title={Approach and Method for Bayesian Network Modelling: The Case for Pregnancy Outcomes in England and Wales},
booktitle={Proceedings of the 17th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 2: HEALTHINF},
year={2024},
pages={604-612},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012428600003657},
isbn={978-989-758-688-0},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 17th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 2: HEALTHINF
TI - Approach and Method for Bayesian Network Modelling: The Case for Pregnancy Outcomes in England and Wales
SN - 978-989-758-688-0
AU - McLachlan S.
AU - Daley B.
AU - Saidi S.
AU - Kyrimi E.
AU - Dube K.
AU - Grossan C.
AU - Neil M.
AU - Rose L.
AU - Fenton N.
PY - 2024
SP - 604
EP - 612
DO - 10.5220/0012428600003657
PB - SciTePress