2 citations · 3 across the 4 of their papers we have counts for
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Data-Driven Priors for Uncertainty-Aware Risk Prediction of Clinical Deterioration using Multimodal Data
L. Julián Lechuga López, Tim G. J. Rudner, Farah E. Shamout
Safe predictions are a crucial requirement for integrating predictive models into clinical decision support systems. One approach to improving trustworthiness is to enable models t…
An Empirical Analysis of Calibration and Selective Prediction in Multimodal Clinical Condition Classification
L. Julián Lechuga López, Farah E. Shamout, Tim G. J. Rudner
As artificial intelligence systems move toward clinical deployment, ensuring reliable prediction behavior is fundamental for safety-critical decision-making tasks. One proposed saf…
Reflections from Research Roundtables at the Conference on Health, Inference, and Learning (CHIL) 2025
Emily Alsentzer, Marie-Laure Charpignon, Bill Chen +90
The 6th Annual Conference on Health, Inference, and Learning (CHIL 2025), hosted by the Association for Health Learning and Inference (AHLI), was held in person on June 25-27, 2025…
Uncertainty Quantification for Machine Learning in Healthcare: A Survey
L. Julián Lechuga López, Shaza Elsharief, Dhiyaa Al Jorf +3
Uncertainty Quantification (UQ) is pivotal in enhancing the robustness, reliability, and interpretability of Machine Learning (ML) systems for healthcare, optimizing resources and…