2 citations · 8 across the 15 of their papers we have counts for
8 papers · 1 filter
Aligning Probabilistic Beliefs under Informative Missingness: LLM Steerability in Clinical Reasoning
Yuta Kobayashi, Vincent Jeanselme, Shalmali Joshi
Large Language Models (LLMs) are increasingly deployed for clinical reasoning tasks, which inherently require eliciting calibrated probabilistic beliefs based on available evidence…
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…
ADHAM: Additive Deep Hazard Analysis Mixtures for Interpretable Survival Regression
Mert Ketenci, Vincent Jeanselme, Harry Reyes Nieva +2
Survival analysis is a fundamental tool for modeling time-to-event outcomes in healthcare. Recent advances have introduced flexible neural network approaches for improved predictiv…
Prediction of Survival Outcomes under Clinical Presence Shift: A Joint Neural Network Architecture
Vincent Jeanselme, Glen Martin, Matthew Sperrin +3
Electronic health records arise from the complex interaction between patients and the healthcare system. This observation process of interactions, referred to as clinical presence,…
Competing Risks: Impact on Risk Estimation and Algorithmic Fairness
Vincent Jeanselme, Brian Tom, Jessica Barrett
Accurate time-to-event prediction is integral to decision-making, informing medical guidelines, hiring decisions, and resource allocation. Survival analysis, the quantitative frame…
Assessing the impact of variance heterogeneity and misspecification in mixed-effects location-scale models
Vincent Jeanselme, Marco Palma, Jessica K Barrett
Linear Mixed Model (LMM) is a common statistical approach to model the relation between exposure and outcome while capturing individual variability through random effects. However,…
ICYM2I: The illusion of multimodal informativeness under missingness
Young Sang Choi, Vincent Jeanselme, Pierre Elias +1
Multimodal learning is of continued interest in artificial intelligence-based applications, motivated by the potential information gain from combining different data modalities. Ho…
FoMoH: A clinically meaningful foundation model evaluation for structured electronic health records
Vincent Jeanselme, Zilin Jing, Aparajita Kashyap +9
Foundation models (FMs) promise to address core limitations of traditional supervised machine learning: (i) reliance on large amounts of labeled data, (ii) task specificity, and (i…