Journal of Imaging (May 2024)

Bayesian Networks in the Management of Hospital Admissions: A Comparison between Explainable AI and Black Box AI during the Pandemic

  • Giovanna Nicora,
  • Michele Catalano,
  • Chandra Bortolotto,
  • Marina Francesca Achilli,
  • Gaia Messana,
  • Antonio Lo Tito,
  • Alessio Consonni,
  • Sara Cutti,
  • Federico Comotto,
  • Giulia Maria Stella,
  • Angelo Corsico,
  • Stefano Perlini,
  • Riccardo Bellazzi,
  • Raffaele Bruno,
  • Lorenzo Preda

DOI
https://doi.org/10.3390/jimaging10050117
Journal volume & issue
Vol. 10, no. 5
p. 117

Abstract

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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 suffers from poor generalization ability in the presence of dataset shifts. 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 of supporting 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.

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