7 papers
Graph Alignment Topology as an Inductive Bias for Grounding Detection
Paul Landes, Pranav Herur, Adam Cross +1
Large Language Models (LLMs) are optimized to produce distributionally plausible continuations rather than to explicitly verify whether generated propositions are entailed by sourc…
RDMA: Cost Effective Agent-Driven Rare Disease Mining from Electronic Health Records
John Wu, Adam Cross, Jimeng Sun
Rare diseases affect 1 in 10 Americans yet remain systematically underdocumented in clinical records. ICD-based systems cannot capture their breadth, over 50\% of Orphanet codes la…
A Practical Guide Towards Interpreting Time-Series Deep Clinical Predictive Models: A Reproducibility Study
Yongda Fan, John Wu, Andrea Fitzpatrick +3
Clinical decisions are high-stakes and require explicit justification, making model interpretability essential for auditing deep clinical models prior to deployment. As the ecosyst…
MIMIC-RD: Can LLMs differentially diagnose rare diseases in real-world clinical settings?
Zilal Eiz AlDin, John Wu, Jeffrey Paul Fung +5
Despite rare diseases affecting 1 in 10 Americans, their differential diagnosis remains challenging. Due to their impressive recall abilities, large language models (LLMs) have bee…
Social Determinants of Health Prediction for ICD-9 Code with Reasoning Models
Sharim Khan, Paul Landes, Adam Cross +1
Social Determinants of Health correlate with patient outcomes but are rarely captured in structured data. Recent attention has been given to automatically extracting these markers…
Integration of Large Language Models and Traditional Deep Learning for Social Determinants of Health Prediction
Paul Landes, Jimeng Sun, Adam Cross
Social Determinants of Health (SDoH) are economic, social and personal circumstances that affect or influence an individual's health status. SDoHs have shown to be correlated to we…