Discernibility in explanations: Designing more acceptable and meaningful machine learning models for medicine

Discernibility in explanations: Designing more acceptable and meaningful machine learning models for medicine

Although the benefits of machine learning are undeniable in healthcare, explainability plays a vital role in improving transparency and understanding the most decisive and persuasive variables for prediction. The challenge is to identify explanations that make sense to the biomedical expert. This wo...

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Bibliographic Details
Main Authors: Haomiao Wang, Julien Aligon, Julien May, Emmanuel Doumard, Nicolas Labroche, Cyrille Delpierre, Chantal Soulé-Dupuy, Louis Casteilla, Valérie Planat-Benard, Paul Monsarrat
Format: Article
Language:English
Published: Elsevier 2025-01-01
Series:Computational and Structural Biotechnology Journal
Subjects:
Discernibility of explanations
Explainable machine learning
Acceptability of artificial intelligence
Feature selection
Online Access:http://www.sciencedirect.com/science/article/pii/S2001037025001412
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