6 papers
OC-Distill: Ontology-aware Contrastive Learning with Cross-Modal Distillation for ICU Risk Prediction
Zhongyuan Liang, Junhyung Jo, Hyang-Jung Lee +2
Early prediction of severe clinical deterioration and remaining length of stay can enable timely intervention and better resource allocation in high-acuity settings such as the ICU…
Local MDI+: Local Feature Importances for Tree-Based Models
Zhongyuan Liang, Zachary T. Rewolinski, Abhineet Agarwal +2
Tree-based ensembles such as random forests remain the go-to for tabular data over deep learning models due to their prediction performance and computational efficiency. These adva…
Do Sparse Autoencoders Identify Reasoning Features in Language Models?
George Ma, Zhongyuan Liang, Irene Y. Chen +1
We study how reliably sparse autoencoders (SAEs) support claims about reasoning-related internal features in large language models. We first give a stylized analysis showing that s…
Hybrid Meta-learners for Estimating Heterogeneous Treatment Effects
Zhongyuan Liang, Lars van der Laan, Ahmed Alaa
Estimating conditional average treatment effects (CATE) from observational data involves modeling decisions that differ from supervised learning, particularly concerning how to reg…
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…
Revealing Treatment Non-Adherence Bias in Clinical Machine Learning Using Large Language Models
Zhongyuan Liang, Arvind Suresh, Irene Y. Chen
Machine learning systems trained on electronic health records (EHRs) increasingly guide treatment decisions, but their reliability depends on the critical assumption that patients…