82 citations · 130 across the 19 of their papers we have counts for
23 papers
RadTimeline: Timeline Summarization for Longitudinal Radiological Lung Findings
Sitong Zhou, Meliha Yetisgen, Mari Ostendorf
Tracking findings in longitudinal radiology reports is crucial for accurately identifying disease progression, and the time-consuming process would benefit from automatic summariza…
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…
Traj-CoA: Patient Trajectory Modeling via Chain-of-Agents for Lung Cancer Risk Prediction
Sihang Zeng, Yujuan Fu, Sitong Zhou +6
Large language models (LLMs) offer a generalizable approach for modeling patient trajectories, but suffer from the long and noisy nature of electronic health records (EHR) data in…
MORQA: Benchmarking Evaluation Metrics for Medical Open-Ended Question Answering
Wen-wai Yim, Asma Ben Abacha, Zixuan Yu +3
Evaluating natural language generation (NLG) systems in the medical domain presents unique challenges due to the critical demands for accuracy, relevance, and domain-specific exper…
Spurious Correlations and Beyond: Understanding and Mitigating Shortcut Learning in SDOH Extraction with Large Language Models
Fardin Ahsan Sakib, Ziwei Zhu, Karen Trister Grace +2
Social determinants of health (SDOH) extraction from clinical text is critical for downstream healthcare analytics. Although large language models (LLMs) have shown promise, they m…
A Scoping Review of Natural Language Processing in Addressing Medically Inaccurate Information: Errors, Misinformation, and Hallucination
Zhaoyi Sun, Wen-Wai Yim, Ozlem Uzuner +2
Objective: This review aims to explore the potential and challenges of using Natural Language Processing (NLP) to detect, correct, and mitigate medically inaccurate information, in…
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…
MEDEC: A Benchmark for Medical Error Detection and Correction in Clinical Notes
Asma Ben Abacha, Wen-wai Yim, Yujuan Fu +4
Several studies showed that Large Language Models (LLMs) can answer medical questions correctly, even outperforming the average human score in some medical exams. However, to our k…
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…
Backdoor Adjustment of Confounding by Provenance for Robust Text Classification of Multi-institutional Clinical Notes
Xiruo Ding, Zhecheng Sheng, Meliha Yetişgen +2
Natural Language Processing (NLP) methods have been broadly applied to clinical tasks. Machine learning and deep learning approaches have been used to improve the performance of cl…
Building blocks for complex tasks: Robust generative event extraction for radiology reports under domain shifts
Sitong Zhou, Meliha Yetisgen, Mari Ostendorf
This paper explores methods for extracting information from radiology reports that generalize across exam modalities to reduce requirements for annotated data. We demonstrate that…
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…
ACI-BENCH: a Novel Ambient Clinical Intelligence Dataset for Benchmarking Automatic Visit Note Generation
Wen-wai Yim, Yujuan Fu, Asma Ben Abacha +3
Recent immense breakthroughs in generative models such as in GPT4 have precipitated re-imagined ubiquitous usage of these models in all applications. One area that can benefit by i…
The Leaf Clinical Trials Corpus: a new resource for query generation from clinical trial eligibility criteria
Nicholas J Dobbins, Tony Mullen, Ozlem Uzuner +1
Identifying cohorts of patients based on eligibility criteria such as medical conditions, procedures, and medication use is critical to recruitment for clinical trials. Such criter…
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…
Jointly Learning Clinical Entities and Relations with Contextual Language Models and Explicit Context
Paul Barry, Sam Henry, Meliha Yetisgen +2
We hypothesize that explicit integration of contextual information into an Multi-task Learning framework would emphasize the significance of context for boosting performance in joi…
Transferability of Neural Network Clinical De-identification Systems
Kahyun Lee, Nicholas J. Dobbins, Bridget McInnes +2
Objective: Neural network de-identification studies have focused on individual datasets. These studies assume the availability of a sufficient amount of human-annotated data to tra…
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…
Extraction and Analysis of Clinically Important Follow-up Recommendations in a Large Radiology Dataset
Wilson Lau, Thomas H Payne, Ozlem Uzuner +1
Communication of follow-up recommendations when abnormalities are identified on imaging studies is prone to error. In this paper, we present a natural language processing approach…