338 citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- The University of Texas MD Anderson Cancer CenterUS17 papers
- The University of Texas Health Science CenterUS15 papers
- Yale UniversityUS13 papers
- Rice UniversityUS12 papers
- Fudan UniversityCN7 papers
- Harvard UniversityUS7 papers
- Mayo Clinic in FloridaUS7 papers
- National Institutes of HealthUS6 papers
- University of PennsylvaniaUS6 papers
- Texas A&M UniversityUS5 papers
- Baylor College of MedicineUS4 papers
- Cornell UniversityUS4 papers
7 papers · 2 filters
Use GPT-J Prompt Generation with RoBERTa for NER Models on Diagnosis Extraction of Periodontal Diagnosis from Electronic Dental Records
Yao-Shun Chuang, Xiaoqian Jiang, Chun-Teh Lee +4
This study explored the usability of prompt generation on named entity recognition (NER) tasks and the performance in different settings of the prompt. The prompt generation by GPT…
AE-GPT: Using Large Language Models to Extract Adverse Events from Surveillance Reports-A Use Case with Influenza Vaccine Adverse Events
Yiming Li, Jianfu Li, Jianping He +1
Though Vaccines are instrumental in global health, mitigating infectious diseases and pandemic outbreaks, they can occasionally lead to adverse events (AEs). Recently, Large Langua…
Text Classification of Cancer Clinical Trial Eligibility Criteria
Yumeng Yang, Soumya Jayaraj, Ethan B Ludmir +1
Automatic identification of clinical trials for which a patient is eligible is complicated by the fact that trial eligibility is stated in natural language. A potential solution to…
Identifying and Extracting Rare Disease Phenotypes with Large Language Models
Cathy Shyr, Yan Hu, Paul A. Harris +1
Rare diseases (RDs) are collectively common and affect 300 million people worldwide. Accurate phenotyping is critical for informing diagnosis and treatment, but RD phenotypes are o…
BiomedGPT: A Generalist Vision-Language Foundation Model for Diverse Biomedical Tasks
Kai Zhang, Rong Zhou, Eashan Adhikarla +20
Traditional biomedical artificial intelligence (AI) models, designed for specific tasks or modalities, often exhibit limited flexibility in real-world deployment and struggle to ut…
Benchmarking large language models for biomedical natural language processing applications and recommendations
Qingyu Chen, Yan Hu, Xueqing Peng +18
The rapid growth of biomedical literature poses challenges for manual knowledge curation and synthesis. Biomedical Natural Language Processing (BioNLP) automates the process. While…
Large Language Models for Healthcare Data Augmentation: An Example on Patient-Trial Matching
Jiayi Yuan, Ruixiang Tang, Xiaoqian Jiang +1
The process of matching patients with suitable clinical trials is essential for advancing medical research and providing optimal care. However, current approaches face challenges s…