This article discusses the need for explainability in machine learning models used for clinical decision-making. The authors present a multimodal masking framework that extends…
Browsing: SHAP
This article discusses the use of Shapley Additive exPlanations (SHAP), a game-theoretic technique, to enhance the interpretability of machine learning models in healthcare. The…
This article compares the approaches of Explainable AI and Generative AI in AI development and their impact on the future of technology. It discusses…
This study aimed to determine the factors that associate serum anti-HSP27 antibody titers using ensemble machine learning methods and to demonstrate the magnitude and…
This study focused on post-menopausal women in the UKB cohort to explore the potential of using Machine Learning (ML) methods for feature selection to…
Explainable AI (XAI) is a rapidly growing field of machine learning that focuses on providing explanations for the decisions and predictions made by artificial…