Explainable AI

LEAD: Localized Explanations with Adversarial Decision Boundary Characterization for Interpretable Disease Prediction
LEAD: Localized Explanations with Adversarial Decision Boundary Characterization for Interpretable Disease Prediction

LEAD is an explainable AI technique that determines relative feature contributions by characterizing the decision boundary and perturbing critical samples along the decision boundary close to the test sample. LEAD achieves at least 6% improved fidelity and 7% improved consistency compared to LIME and SHAP.

Apr 7, 2025
Use of Machine Learning to Predict Medication Adherence in Individuals at Risk for Atherosclerotic Cardiovascular Disease
Use of Machine Learning to Predict Medication Adherence in Individuals at Risk for Atherosclerotic Cardiovascular Disease

Briefly evaluates the factors behind medication adherence of patients at risk for atherosclerotic cardiovascular disease.

Sep 25, 2022