Nicora, G.; Catalano, M.; Bortolotto, C.; Achilli, M.F.; Messana, G.; Lo Tito, A.; Consonni, A.; Cutti, S.; Comotto, F.; Stella, G.M.; Corsico, A.; Perlini, S.; Bellazzi, R.; Bruno, R.; Preda, L. Bayesian Networks in the Management of Hospital Admissions: A Comparison between Explainable AI and Black Box AI during the Pandemic. J. Imaging2024, 10, 117.
Nicora, G.; Catalano, M.; Bortolotto, C.; Achilli, M.F.; Messana, G.; Lo Tito, A.; Consonni, A.; Cutti, S.; Comotto, F.; Stella, G.M.; Corsico, A.; Perlini, S.; Bellazzi, R.; Bruno, R.; Preda, L. Bayesian Networks in the Management of Hospital Admissions: A Comparison between Explainable AI and Black Box AI during the Pandemic. J. Imaging 2024, 10, 117.
Nicora, G.; Catalano, M.; Bortolotto, C.; Achilli, M.F.; Messana, G.; Lo Tito, A.; Consonni, A.; Cutti, S.; Comotto, F.; Stella, G.M.; Corsico, A.; Perlini, S.; Bellazzi, R.; Bruno, R.; Preda, L. Bayesian Networks in the Management of Hospital Admissions: A Comparison between Explainable AI and Black Box AI during the Pandemic. J. Imaging2024, 10, 117.
Nicora, G.; Catalano, M.; Bortolotto, C.; Achilli, M.F.; Messana, G.; Lo Tito, A.; Consonni, A.; Cutti, S.; Comotto, F.; Stella, G.M.; Corsico, A.; Perlini, S.; Bellazzi, R.; Bruno, R.; Preda, L. Bayesian Networks in the Management of Hospital Admissions: A Comparison between Explainable AI and Black Box AI during the Pandemic. J. Imaging 2024, 10, 117.
Abstract
Artificial Intelligence (AI) and Machine Learning (ML) approaches that could learn from large data sources have been identified as useful tools to support clinicians in their decisional process; AI and ML implementations have had a rapid acceleration during the recent COVID-19 pandemic. However, many ML classifiers are “black box” to the final user, since their underlying reasoning process is often obscure. Additionally, the performance of such models suffer from poor generalization ability in presence of dataset shift. Here, we present a comparison between an explainable-by-design (“white box”) model (Bayesian Network (BN)) versus a black-box model (Random Forest), both studied with the aim to support clinicians of Policlinico San Matteo University Hospital in Pavia (Italy) during the triage of COVID-19 patients. Our aim is to evaluate whether the BN predictive performances are comparable with those of a widely used but less explainable ML model such as Random Forest and to test the generalization ability of the ML models across different waves of the pandemic.
Keywords
Artificial Intelligence; Explainability; Machine Learning; Random Forest; Bayesian Networks; COVID-19
Subject
Medicine and Pharmacology, Emergency Medicine
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.