51 citations · 112 across the 28 of their papers we have counts for
5 papers · 1 filter
Realistic CDSS Drug Dosing with End-to-end Recurrent Q-learning for Dual Vasopressor Control
Will Y. Zou, Jean Feng, Alexandre Kalimouttou +3
Reinforcement learning (RL) applications in Clinical Decision Support Systems (CDSS) frequently encounter skepticism because models may recommend inoperable dosing decisions. We pr…
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…
When the Domain Expert Has No Time and the LLM Developer Has No Clinical Expertise: Real-World Lessons from LLM Co-Design in a Safety-Net Hospital
Avni Kothari, Patrick Vossler, Jean Digitale +8
Large language models (LLMs) have the potential to address social and behavioral determinants of health by transforming labor intensive workflows in resource-constrained settings.…
"Who experiences large model decay and why?" A Hierarchical Framework for Diagnosing Heterogeneous Performance Drift
Harvineet Singh, Fan Xia, Alexej Gossmann +3
Machine learning (ML) models frequently experience performance degradation when deployed in new contexts. Such degradation is rarely uniform: some subgroups may suffer large perfor…
LLMs Judging LLMs: A Simplex Perspective
Patrick Vossler, Fan Xia, Yifan Mai +2
Given the challenge of automatically evaluating free-form outputs from large language models (LLMs), an increasingly common solution is to use LLMs themselves as the judging mechan…