Data-Driven Priors for Uncertainty-Aware Deterioration Risk Prediction with Multimodal Data

ArXi:2603.08459v1 Announce Type: new Safe predictions are a crucial requirement for integrating predictive models into clinical decision systems. One approach for ensuring trustworthiness is to enable models' ability to express their uncertainty about individual predictions. However, current machine learning models frequently lack reliable uncertainty estimation, hindering real-world deployment. This is further observed in multimodal settings, where the goal is to enable effective information fusion.