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cs.LG2025
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…
cs.LG2025★ 1 cited
Reliably Detecting Model Failures in Deployment Without Labels
Viet Nguyen, Changjian Shui, Vijay Giri +4
The distribution of data changes over time; models operating in dynamic environments need retraining. But knowing when to retrain, without access to labels, is an open challenge si…