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
Department of Electrical, Computer and Biomedical Engineering, University of Pavia, 27100 Pavia, Italy.
Diagnostic Imaging and Radiotherapy Unit, Department of Clinical, Surgical, Diagnostic and Pediatric Sciences, University of Pavia, 27100 Pavia, Italy.
J Imaging. 2024 May 10;10(5):117. doi: 10.3390/jimaging10050117.
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.
能够从大量数据源中学习的人工智能（AI）和机器学习（ML）方法已被视为在临床医生决策过程中提供支持的有用工具；在最近的新冠疫情期间，AI和ML的应用迅速加速。然而，许多ML分类器对于最终用户来说是“黑匣子”，因为其底层推理过程往往晦涩难懂。此外，在数据集发生变化时，此类模型的性能会受到泛化能力差的影响。在此，我们对一个设计上可解释的（“白匣子”）模型（贝叶斯网络（BN））和一个黑匣子模型（随机森林）进行了比较，二者的研究目的均是在意大利帕维亚圣马泰奥大学医院对新冠患者进行分诊时为临床医生提供支持。我们的目的是评估BN的预测性能是否与广泛使用但较难解释的ML模型（如随机森林）相当，并测试ML模型在疫情不同阶段的泛化能力。
