ScoresActivation: A New Activation Function for Model Agnostic Global Explainability by Design

The Temporal Fusion Nexus (TFN) has been introduced as a multi-modal and task-agnostic embedding model designed to integrate irregular time series data and unstructured clinical narratives, specifically in the context of post-kidney transplant care. In a study involving 3,382 patients, TFN demonstrated superior performance in predicting graft loss, graft rejection, and mortality compared to existing models, achieving AUC scores of 0.96, 0.84, and 0.86 respectively.

A novel approach to explainable artificial intelligence (AI) has been proposed, leveraging Quantum Boltzmann Machines (QBMs) and Classical Boltzmann Machines (CBMs) to enhance decision-making transparency. This framework utilizes gradient-based saliency maps and SHAP for feature attribution, addressing the critical challenge of explainability in high-stakes domains like healthcare and finance.

The $ ext{phi}$-test has been introduced as a global feature-selection and significance procedure designed for black-box predictors, integrating Shapley attributions with selective inference. It operates by screening features guided by SHAP and fitting a linear surrogate model, providing a comprehensive global feature-importance table with Shapley-based scores and statistical significance metrics.