SurvSHAP: A Proxy-Based Algorithm for Explaining Survival Models with SHAP. - Abdallah Alabdallah; Sepideh Pashami; Thorsteinn S. Rögnvaldsson; Mattias Ohlsson (DSAA, 2022)

Explainable Predictive Maintenance. - Sepideh Pashami; Slawomir Nowaczyk; Yuantao Fan; Jakub Jakubowski; Nuno Paiva; Narjes Davari; Szymon Bobek; Samaneh Jamshidi; Hamid Sarmadi; Abdallah Alabdallah; Rita P. Ribeiro; Bruno Veloso; et al. (CoRR, 2023)

Heterogeneous Federated Learning via Personalized Generative Networks. - Zahra Taghiyarrenani; Abdallah Alabdallah; Slawomir Nowaczyk; Sepideh Pashami (CoRR, 2023)

Improving Concordance Index in Regression-based Survival Analysis: Evolutionary Discovery of Loss Function for Neural Networks. - Mohammed Ghaith Altarabichi; Abdallah Alabdallah; Sepideh Pashami; Thorsteinn S. Rögnvaldsson; Slawomir Nowaczyk; Mattias Ohlsson (GECCO Companion, 2024)

Understanding Survival Models Through Counterfactual Explanations. - Abdallah Alabdallah; Jakub Jakubowski; Sepideh Pashami; Szymon Bobek; Mattias Ohlsson; Thorsteinn S. Rögnvaldsson; Grzegorz J. Nalepa (ICCS (4), 2024)

The Concordance Index decomposition: A measure for a deeper understanding of survival prediction models. - Abdallah Alabdallah; Mattias Ohlsson; Sepideh Pashami; Thorsteinn S. Rögnvaldsson (Artif. Intell. Medicine, 2024)

CoxSE: Exploring the Potential of Self-Explaining Neural Networks with Cox Proportional Hazards Model for Survival Analysis. - Abdallah Alabdallah; Omar Hamed; Mattias Ohlsson; Thorsteinn S. Rögnvaldsson; Sepideh Pashami (CoRR, 2024)

Towards Trustworthy Survival Analysis with Machine Learning Models - Abdallah Alabdallah (2025)