48 citations · 56 across the 9 of their papers we have counts for
4 papers · 1 filter
Interpretable Fine-Gray Deep Survival Model for Competing Risks: Predicting Post-Discharge Foot Complications for Diabetic Patients in Ontario
Dhanesh Ramachandram, Anne Loefler, Surain Roberts +5
Model interpretability is crucial for establishing AI safety and clinician trust in medical applications for example, in survival modelling with competing risks. Recent deep learni…
Transparent AI: The Case for Interpretability and Explainability
Dhanesh Ramachandram, Himanshu Joshi, Judy Zhu +3
As artificial intelligence systems increasingly inform high-stakes decisions across sectors, transparency has become foundational to responsible and trustworthy AI implementation.…
Bridging Electronic Health Records and Clinical Texts: Contrastive Learning for Enhanced Clinical Tasks
Sara Ketabi, Dhanesh Ramachandram
Conventional machine learning models, particularly tree-based approaches, have demonstrated promising performance across various clinical prediction tasks using electronic health r…
CRISP-NAM: Competing Risks Interpretable Survival Prediction with Neural Additive Models
Dhanesh Ramachandram, Ananya Raval
Competing risks are crucial considerations in survival modelling, particularly in healthcare domains where patients may experience multiple distinct event types. We propose CRISP-N…