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

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, man...

Full description

Saved in:
Bibliographic Details
Published in:Journal of imaging 2024-05, Vol.10 (5), p.117
Main Authors: Nicora, Giovanna, Catalano, Michele, Bortolotto, Chandra, Achilli, Marina Francesca, Messana, Gaia, Lo Tito, Antonio, Consonni, Alessio, Cutti, Sara, Comotto, Federico, Stella, Giulia Maria, Corsico, Angelo, Perlini, Stefano, Bellazzi, Riccardo, Bruno, Raffaele, Preda, Lorenzo
Format: Article
Language:English
Subjects:
Admission and discharge
Algorithms
Artificial intelligence
Bayesian analysis
Bayesian Networks
Black boxes
Comparative analysis
COVID-19
Decision making
Decision trees
Deep learning
explainability
Explainable artificial intelligence
Hospitals
Machine learning
Pandemics
Patient admissions
Performance prediction
Random Forest
Severe acute respiratory syndrome coronavirus 2
Variables
Citations: Items that this one cites
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
ISSN:2313-433X
2313-433X
DOI:10.3390/jimaging10050117