Comparison of machine learning techniques to predict all-cause mortality using fitness data: the Henry ford exercIse testing (FIT) project. - Sherif Sakr; Radwa El Shawi; Amjad M. Ahmed; Waqas T. Qureshi; Clinton A. Brawner; Steven J. Keteyian; Michael J. Blaha; Mouaz H. Al-Mallah (BMC Medical Informatics Decis. Mak., 2017)

ILIME: Local and Global Interpretable Model-Agnostic Explainer of Black-Box Decision. - Radwa El Shawi; Youssef Sherif; Mouaz H. Al-Mallah; Sherif Sakr (ADBIS, 2019)

LDLCT An Instance-Based Framework for Lesion Detection on Lung CT Scans. - Tarun Khajuria; Eman Badr; Mouaz H. Al-Mallah; Sherif Sakr (CBMS, 2019)

Interpretability in HealthCare A Comparative Study of Local Machine Learning Interpretability Techniques. - Radwa El Shawi; Youssef Sherif; Mouaz H. Al-Mallah; Sherif Sakr (CBMS, 2019)

On the interpretability of machine learning-based model for predicting hypertension. - Radwa El Shawi; Mouaz H. Al-Mallah; Sherif Sakr (BMC Medical Informatics Decis. Mak., 2019)

Interpretable Local Concept-based Explanation with Human Feedback to Predict All-cause Mortality. - Radwa El Shawi; Mouaz H. Al-Mallah (J. Artif. Intell. Res., 2022)

Interpretable Local Concept-based Explanation with Human Feedback to Predict All-cause Mortality (Extended Abstract). - Radwa El Shawi; Mouaz H. Al-Mallah (IJCAI, 2023)