48 citations · 66 across the 17 of their papers we have counts for
17 papers
Traj-Evolve: A Self-Evolving Multi-Agent System for Patient Trajectory Modeling in Lung Cancer Early Detection
Sihang Zeng, Matthew Thompson, Ruth Etzioni +1
Modeling patient trajectories from longitudinal electronic health records (EHRs) requires reasoning over sparse, noisy, and long-context multimodal sequences. Existing LLM-based mu…
RADAR: A Multimodal Benchmark for 3D Image-Based Radiology Report Review
Zhaoyi Sun, Minal Jagtiani, Wen-wai Yim +4
Radiology reports for the same patient examination may contain clinically meaningful discrepancies arising from interpretation differences, reporting variability, or evolving asses…
DermaVQA-DAS: Dermatology Assessment Schema (DAS) & Datasets for Closed-Ended Question Answering & Segmentation in Patient-Generated Dermatology Images
Wen-wai Yim, Yujuan Fu, Asma Ben Abacha +4
Recent advances in dermatological image analysis have been driven by large-scale annotated datasets; however, most existing benchmarks focus on dermatoscopic images and lack patien…
UW-BioNLP at ChemoTimelines 2025: Thinking, Fine-Tuning, and Dictionary-Enhanced LLM Systems for Chemotherapy Timeline Extraction
Tianmai M. Zhang, Zhaoyi Sun, Sihang Zeng +5
The ChemoTimelines shared task benchmarks methods for constructing timelines of systemic anticancer treatment from electronic health records of cancer patients. This paper describe…
A Systematic Analysis of Large Language Models with RAG-enabled Dynamic Prompting for Medical Error Detection and Correction
Farzad Ahmed, Joniel Augustine Jerome, Meliha Yetisgen +1
Objective: Clinical documentation contains factual, diagnostic, and management errors that can compromise patient safety. Large language models (LLMs) may help detect and correct s…
TrajSurv: Learning Continuous Latent Trajectories from Electronic Health Records for Trustworthy Survival Prediction
Sihang Zeng, Lucas Jing Liu, Jun Wen +3
Trustworthy survival prediction is essential for clinical decision making. Longitudinal electronic health records (EHRs) provide a uniquely powerful opportunity for the prediction.…
Large Language Model-Based Agents for Automated Research Reproducibility: An Exploratory Study in Alzheimer's Disease
Nic Dobbins, Christelle Xiong, Kristine Lan +1
Objective: To demonstrate the capabilities of Large Language Models (LLMs) as autonomous agents to reproduce findings of published research studies using the same or similar datase…
Does Data Contamination Detection Work (Well) for LLMs? A Survey and Evaluation on Detection Assumptions
Yujuan Fu, Ozlem Uzuner, Meliha Yetisgen +1
Large language models (LLMs) have demonstrated great performance across various benchmarks, showing potential as general-purpose task solvers. However, as LLMs are typically traine…
LeafAI: query generator for clinical cohort discovery rivaling a human programmer
Nicholas J Dobbins, Bin Han, Weipeng Zhou +5
Objective: Identifying study-eligible patients within clinical databases is a critical step in clinical research. However, accurate query design typically requires extensive techni…
The 2022 n2c2/UW Shared Task on Extracting Social Determinants of Health
Kevin Lybarger, Meliha Yetisgen, Özlem Uzuner
Objective: The n2c2/UW SDOH Challenge explores the extraction of social determinant of health (SDOH) information from clinical notes. The objectives include the advancement of natu…
Leveraging Natural Language Processing to Augment Structured Social Determinants of Health Data in the Electronic Health Record
Kevin Lybarger, Nicholas J Dobbins, Ritche Long +4
Objective: Social determinants of health (SDOH) impact health outcomes and are documented in the electronic health record (EHR) through structured data and unstructured clinical no…
Generalizing through Forgetting -- Domain Generalization for Symptom Event Extraction in Clinical Notes
Sitong Zhou, Kevin Lybarger, Meliha Yetisgen +1
Symptom information is primarily documented in free-text clinical notes and is not directly accessible for downstream applications. To address this challenge, information extractio…
Extracting Radiological Findings With Normalized Anatomical Information Using a Span-Based BERT Relation Extraction Model
Kevin Lybarger, Aashka Damani, Martin Gunn +2
Medical imaging is critical to the diagnosis and treatment of numerous medical problems, including many forms of cancer. Medical imaging reports distill the findings and observatio…
Identifying ARDS using the Hierarchical Attention Network with Sentence Objectives Framework
Kevin Lybarger, Linzee Mabrey, Matthew Thau +3
Acute respiratory distress syndrome (ARDS) is a life-threatening condition that is often undiagnosed or diagnosed late. ARDS is especially prominent in those infected with COVID-19…
Performance of Automatic De-identification Across Different Note Types
Nicholas Dobbins, David Wayne, Kahyun Lee +2
Free-text clinical notes detail all aspects of patient care and have great potential to facilitate quality improvement and assurance initiatives as well as advance clinical researc…
Extracting COVID-19 Diagnoses and Symptoms From Clinical Text: A New Annotated Corpus and Neural Event Extraction Framework
Kevin Lybarger, Mari Ostendorf, Matthew Thompson +1
Coronavirus disease 2019 (COVID-19) is a global pandemic. Although much has been learned about the novel coronavirus since its emergence, there are many open questions related to t…
Automatic Assignment of Radiology Examination Protocols Using Pre-trained Language Models with Knowledge Distillation
Wilson Lau, Laura Aaltonen, Martin Gunn +1
Selecting radiology examination protocol is a repetitive, and time-consuming process. In this paper, we present a deep learning approach to automatically assign protocols to comput…