82 citations · 162 across the 16 of their papers we have counts for
18 papers
Automated Identification of Incidentalomas Requiring Follow-Up: A Multi-Anatomy Evaluation of LLM-Based and Supervised Approaches
Namu Park, Farzad Ahmed, Zhaoyi Sun +6
Objective: To evaluate large language models (LLMs) against supervised baselines for fine-grained, lesion-level detection of incidentalomas requiring follow-up, addressing the limi…
Identifying Imaging Follow-Up in Radiology Reports: A Comparative Analysis of Traditional ML and LLM Approaches
Namu Park, Giridhar Kaushik Ramachandran, Kevin Lybarger +4
Large language models (LLMs) have shown considerable promise in clinical natural language processing, yet few domain-specific datasets exist to rigorously evaluate their performanc…
Adapting Biomedical Abstracts into Plain language using Large Language Models
Haritha Gangavarapu, Giridhar Kaushik Ramachandran, Kevin Lybarger +2
A vast amount of medical knowledge is available for public use through online health forums, and question-answering platforms on social media. The majority of the population in the…
BioMistral-NLU: Towards More Generalizable Medical Language Understanding through Instruction Tuning
Yujuan Velvin Fu, Giridhar Kaushik Ramachandran, Namu Park +4
Large language models (LLMs) such as ChatGPT are fine-tuned on large and diverse instruction-following corpora, and can generalize to new tasks. However, those instruction-tuned LL…
CACER: Clinical Concept Annotations for Cancer Events and Relations
Yujuan Fu, Giridhar Kaushik Ramachandran, Ahmad Halwani +5
Clinical notes contain unstructured representations of patient histories, including the relationships between medical problems and prescription drugs. To investigate the relationsh…
Extracting Social Determinants of Health from Pediatric Patient Notes Using Large Language Models: Novel Corpus and Methods
Yujuan Fu, Giridhar Kaushik Ramachandran, Nicholas J Dobbins +7
Social determinants of health (SDoH) play a critical role in shaping health outcomes, particularly in pediatric populations where interventions can have long-term implications. SDo…
A Novel Corpus of Annotated Medical Imaging Reports and Information Extraction Results Using BERT-based Language Models
Namu Park, Kevin Lybarger, Giridhar Kaushik Ramachandran +5
Medical imaging is critical to the diagnosis, surveillance, and treatment of many health conditions, including oncological, neurological, cardiovascular, and musculoskeletal disord…
Prompt-based Extraction of Social Determinants of Health Using Few-shot Learning
Giridhar Kaushik Ramachandran, Yujuan Fu, Bin Han +4
Social determinants of health (SDOH) documented in the electronic health record through unstructured text are increasingly being studied to understand how SDOH impacts patient heal…
MasonNLP+ at SemEval-2023 Task 8: Extracting Medical Questions, Experiences and Claims from Social Media using Knowledge-Augmented Pre-trained Language Models
Giridhar Kaushik Ramachandran, Haritha Gangavarapu, Kevin Lybarger +1
In online forums like Reddit, users share their experiences with medical conditions and treatments, including making claims, asking questions, and discussing the effects of treatme…
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 Medication Changes in Clinical Narratives using Pre-trained Language Models
Giridhar Kaushik Ramachandran, Kevin Lybarger, Yaya Liu +5
An accurate and detailed account of patient medications, including medication changes within the patient timeline, is essential for healthcare providers to provide appropriate pati…
Event-based clinical findings extraction from radiology reports with pre-trained language model
Wilson Lau, Kevin Lybarger, Martin L. Gunn +1
Radiology reports contain a diverse and rich set of clinical abnormalities documented by radiologists during their interpretation of the images. Comprehensive semantic representati…
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…
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…
Annotating Social Determinants of Health Using Active Learning, and Characterizing Determinants Using Neural Event Extraction
Kevin Lybarger, Mari Ostendorf, Meliha Yetisgen
Social determinants of health (SDOH) affect health outcomes, and knowledge of SDOH can inform clinical decision-making. Automatically extracting SDOH information from clinical text…