172 citations · 287 across the 10 of their papers we have counts for
7 papers · 1 filter
BioPulse-QA: A Dynamic Biomedical Question-Answering Benchmark for Evaluating Factuality, Robustness, and Bias in Large Language Models
Kriti Bhattarai, Vipina K. Keloth, Donald Wright +3
Objective: Large language models (LLMs) are increasingly applied in biomedical settings, and existing benchmark datasets have played an important role in supporting model developme…
EHRNavigator: A Multi-Agent System for Patient-Level Clinical Question Answering over Heterogeneous Electronic Health Records
Lingfei Qian, Mauro Giuffre, Yan Wang +11
Clinical decision-making increasingly relies on timely and context-aware access to patient information within Electronic Health Records (EHRs), yet most existing natural language q…
Toward Automated Cognitive Assessment in Parkinson's Disease Using Pretrained Language Models
Varada Khanna, Nilay Bhatt, Ikgyu Shin +4
Understanding how individuals with Parkinson's disease (PD) describe cognitive experiences in their daily lives can offer valuable insights into disease-related cognitive and emoti…
Information Extraction from Clinical Notes: Are We Ready to Switch to Large Language Models?
Yan Hu, Xu Zuo, Yujia Zhou +9
Backgrounds: Information extraction (IE) is critical in clinical natural language processing (NLP). While large language models (LLMs) excel on generative tasks, their performance…
Me LLaMA: Foundation Large Language Models for Medical Applications
Qianqian Xie, Qingyu Chen, Aokun Chen +15
Recent advancements in large language models (LLMs) like ChatGPT and LLaMA show promise in medical applications, yet challenges remain in medical language comprehension. This study…
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…
Improving Large Language Models for Clinical Named Entity Recognition via Prompt Engineering
Yan Hu, Qingyu Chen, Jingcheng Du +9
Objective: This study quantifies the capabilities of GPT-3.5 and GPT-4 for clinical named entity recognition (NER) tasks and proposes task-specific prompts to improve their perform…