Explainable AI

This paper studies how to improve counterfactual explanations with feasibility and diversity constraints.

This paper uses SHAP and counterfactual explanation to analyze dementia risk in diabetic and non-diabetic populations..

Developing interactive and explainable algorithms for survival analysis with applications in equipment failure prediction, customer churn analytics, risk forecasting, and patient outcome modeling.

This paper assesses hospital length of stay using interpretable survival analysis.