research-article

A Bayesian network model for predicting cardiovascular risk

Authors:

A. Santos-Lozano,

J.M. CamachoAuthors Info & Claims

Volume 231, Issue C

https://doi.org/10.1016/j.cmpb.2023.107405

Published: 01 April 2023 Publication History

Highlights

•

Analysis of cardiovascular risk factors through a Bayesian network.

•

Accessible model for the diagnosis of CV medical conditions.

•

Probabilistic tool to support research decisions.

•

Support system to decide the most appropriate treatment.

•

Free software with implemented model.

Abstract

Background and Objective

Cardiovascular diseases are the leading death cause in Europe and entail large treatment costs. Cardiovascular risk prediction is crucial for the management and control of cardiovascular diseases. Based on a Bayesian network built from a large population database and expert judgment, this work studies interrelations between cardiovascular risk factors, emphasizing the predictive assessment of medical conditions, and providing a computational tool to explore and hypothesize such interrelations.

Methods

We implement a Bayesian network model that considers modifiable and non-modifiable cardiovascular risk factors as well as related medical conditions. Both the structure and the probability tables in the underlying model are built using a large dataset collected from annual work health assessments as well as expert information, with uncertainty characterized through posterior distributions.

Results

The implemented model allows for making inferences and predictions about cardiovascular risk factors. The model can be utilized as a decision- support tool to suggest diagnosis, treatment, policy, and research hypothesis. The work is complemented with a free software implementing the model for practitioners’ use.

Conclusions

Our implementation of the Bayesian network model facilitates answering public health, policy, diagnosis, and research questions concerning cardiovascular risk factors.

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Cited By

Chu HBuchan ESmith DGoldberg Oppenheimer P(2024)Development and application of an optimised Bayesian shrinkage prior for spectroscopic biomedical diagnosticsComputer Methods and Programs in Biomedicine10.1016/j.cmpb.2024.108014245:COnline publication date: 16-May-2024
https://dl.acm.org/doi/10.1016/j.cmpb.2024.108014
Kassem KSperti MCavallo AVergani AFassino DMoz MLiscio ABanali RDahlweid MBenetti LBruno FGallone GDe Filippo OIannaccone MD'Ascenzo FDe Ferrari GMorbiducci UDella Valle EDeriu M(2024)An innovative artificial intelligence-based method to compress complex models into explainable, model-agnostic and reduced decision support systems with application to healthcare (NEAR)Artificial Intelligence in Medicine10.1016/j.artmed.2024.102841151:COnline publication date: 1-May-2024
https://dl.acm.org/doi/10.1016/j.artmed.2024.102841

Index Terms

A Bayesian network model for predicting cardiovascular risk

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Published In

cover image Computer Methods and Programs in Biomedicine

Computer Methods and Programs in Biomedicine Volume 231, Issue C

Apr 2023

629 pages

ISSN:0169-2607

Issue’s Table of Contents

Copyright © 2023.

Publisher

Elsevier North-Holland, Inc.

United States

Publication History

Published: 01 April 2023

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Cited By

Chu HBuchan ESmith DGoldberg Oppenheimer P(2024)Development and application of an optimised Bayesian shrinkage prior for spectroscopic biomedical diagnosticsComputer Methods and Programs in Biomedicine10.1016/j.cmpb.2024.108014245:COnline publication date: 16-May-2024
https://dl.acm.org/doi/10.1016/j.cmpb.2024.108014
Kassem KSperti MCavallo AVergani AFassino DMoz MLiscio ABanali RDahlweid MBenetti LBruno FGallone GDe Filippo OIannaccone MD'Ascenzo FDe Ferrari GMorbiducci UDella Valle EDeriu M(2024)An innovative artificial intelligence-based method to compress complex models into explainable, model-agnostic and reduced decision support systems with application to healthcare (NEAR)Artificial Intelligence in Medicine10.1016/j.artmed.2024.102841151:COnline publication date: 1-May-2024
https://dl.acm.org/doi/10.1016/j.artmed.2024.102841

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