Título : Bayesian Networks for the Diagnosis and Prognosis of
Diseases: A Scoping Review |
Autor : Polotskaya, Kristina  Muñoz Valencia, Carlos Segundo  Rabasa, Alejandro  Quesada-Rico, Jose Antonio Orozco-Beltran, Domingo  Barber, Xavier  |
Editor : MDPI |
Departamento: Departamentos de la UMH::Estadística, Matemáticas e Informática |
Fecha de publicación: 2024-06 |
URI : https://hdl.handle.net/11000/34507 |
Resumen :
Bayesian networks (BNs) are probabilistic graphical models that leverage Bayes’ theorem
to portray dependencies and cause-and-effect relationships between variables. These networks have
gained prominence in the field of health sciences, particularly in diagnostic processes, by allowing
the integration of medical knowledge into models and addressing uncertainty in a probabilistic
manner. Objectives: This review aims to provide an exhaustive overview of the current state of
Bayesian networks in disease diagnosis and prognosis. Additionally, it seeks to introduce readers to
the fundamental methodology of BNs, emphasising their versatility and applicability across varied
medical domains. Employing a meticulous search strategy with MeSH descriptors in diverse scientific
databases, we identified 190 relevant references. These were subjected to a rigorous analysis, resulting
in the retention of 60 papers for in-depth review. The robustness of our approach minimised the risk
of selection bias. Results: The selected studies encompass a wide range of medical areas, providing
insights into the statistical methodology, implementation feasibility, and predictive accuracy of BNs,
as evidenced by an average area under the curve (AUC) exceeding 75%. The comprehensive analysis
underscores the adaptability and efficacy of Bayesian networks in diverse clinical scenarios. The
majority of the examined studies demonstrate the potential of BNs as reliable adjuncts to clinical
decision-making. The findings of this review affirm the role of Bayesian networks as accessible and
versatile artificial intelligence tools in healthcare. They offer a viable solution to address complex
medical challenges, facilitating timely and informed decision-making under conditions of uncertainty.
The extensive exploration of Bayesian networks presented in this review highlights their significance
and growing impact in the realm of disease diagnosis and prognosis. It underscores the need for
further research and development to optimise their capabilities and broaden their applicability in
addressing diverse and intricate healthcare challenges.
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Palabras clave/Materias: Bayesian networks disease diagnosis disease prognosis directed acyclic graph Bayesian classifier scoping review |
Área de conocimiento : CDU: Ciencias puras y naturales: Generalidades sobre las ciencias puras |
Tipo de documento : info:eu-repo/semantics/article |
Derechos de acceso: info:eu-repo/semantics/openAccess |
DOI : https://doi.org/10.3390/ make6020058 |
Aparece en las colecciones: Artículos Estadística, Matemáticas e Informática
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