10 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…
EpiGraph: Building Generalists for Evidence-Intensive Epilepsy Reasoning in the Wild
Yuyang Dai, Zheng Chen, Jathurshan Pradeepkumar +4
Epilepsy diagnosis and treatment require evidence-intensive reasoning across heterogeneous clinical knowledge, including biosignal patterns, genetic mechanisms, pharmacogenomics, t…
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…
Making Conformal Predictors Robust in Healthcare Settings: a Case Study on EEG Classification
Arjun Chatterjee, Sayeed Sajjad Razin, John Wu +3
Quantifying uncertainty in clinical predictions is critical for high-stakes diagnosis tasks. Conformal prediction offers a principled approach by providing prediction sets with the…
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…
Bridging the Reproducibility Divide: Open Source Software's Role in Standardizing Healthcare AI
John Wu, Zhenbang Wu, Jimeng Sun
Our analysis of recent AI4H publications reveals that, despite a trend toward utilizing open datasets and sharing modeling code, 74% of AI4H papers still rely on private datasets o…