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Author

G. Sivaramakrishnan

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#explainable ai Sep 2026

Machine learning integrated explainable artificial intelligence in predicting toxicities of enfortumab vedotin in urothelial carcinoma: an exploratory study.

BACKGROUND Enfortumab vedotin (EV) therapy for advanced urothelial carcinoma is limited by adverse events (AEs). Early identification of high-risk patients is needed. This proof-of-concept study evaluated whether machine learning (ML) with explainable AI (SHAP) could predict EV toxicities using real-world data. RESEA...

K. Sridharan, Mattia Alberto Di Civita, G. Sivaramakrishnan et al. · 0 citations
#explainable ai Open access Sep 2026

Machine Learning and Explainable AI for Predicting Survival and Mortality in People Living With HIV on Antiretroviral Therapy: A Secondary Analysis of the ACTG-175 Trial Dataset.

Background Identifying reliable predictors of mortality and survival in people living with HIV (PLWH) is essential for personalized risk stratification and treatment optimization. This study employed machine learning (ML) and explainable artificial intelligence (XAI) to identify key prognostic factors and develop predi...

K. Sridharan, G. Sivaramakrishnan · 0 citations

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