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…
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…
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…
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…
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…
Beyond Label Attention: Transparency in Language Models for Automated Medical Coding via Dictionary Learning
John Wu, David Wu, Jimeng Sun
Medical coding, the translation of unstructured clinical text into standardized medical codes, is a crucial but time-consuming healthcare practice. Though large language models (LL…
DILA: Dictionary Label Attention for Mechanistic Interpretability in High-dimensional Multi-label Medical Coding Prediction
John Wu, David Wu, Jimeng Sun
Predicting high-dimensional or extreme multilabels, such as in medical coding, requires both accuracy and interpretability. Existing works often rely on local interpretability meth…