Synergies Between Artificial Intelligence and Software Engineering: Evolution and Trends

Ramírez, Aurora; Romero, José Raúl

doi:10.1007/978-3-031-08202-3_2

Cited by 3 publications

(1 citation statement)

References 104 publications

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“…BNs were introduced in the 1980s as a formalism for representation and reasoning through problems involving uncertainty, by adopting probability theory as a basic framework [2]. Since then, they have not only been used successfully in medicine; they have also been central to the development of numerous applications in other fields, including environmental sciences [3], production processes [4], finance [5], the debugging of artificial intelligence programs [6], and genetics [7]. In the health sciences, BNs are applied to improve treatments, diagnosis, and prognosis, helping physicians to make reliable decisions by enabling faster and more accurate prediction [8].…”

Section: Introductionmentioning

confidence: 99%

Bayesian Networks for the Diagnosis and Prognosis of Diseases: A Scoping Review

Polotskaya,

Muñoz-Valencia,

Rabasa

et al. 2024

MAKE

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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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