172 citations · 240 across the 7 of their papers we have counts for
7 papers
MedPMC: A Systematic Framework for Scaling High-Fidelity Medical Multimodal Data for Foundation Models
Hyunjae Kim, Dain Kim, Pan Xiao +25
Medicine is inherently multimodal, requiring clinicians to synthesize information across diverse data streams. Yet the development of multimodal foundation models is constrained by…
Can LLM Agents Generate Real-World Evidence? Evaluating Observational Studies in Medical Databases
Dubai Li, Yuxiang He, Yan Hu +2
Observational studies can yield clinically actionable evidence at scale, but executing them on real-world databases is open-ended and requires coherent decisions across cohort cons…
An artificial intelligence framework for end-to-end rare disease phenotyping from clinical notes using large language models
Cathy Shyr, Yan Hu, Rory J. Tinker +8
Phenotyping is fundamental to rare disease diagnosis, but manual curation of structured phenotypes from clinical notes is labor-intensive and difficult to scale. Existing artificia…
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…
A Span-based Model for Extracting Overlapping PICO Entities from RCT Publications
Gongbo Zhang, Yiliang Zhou, Yan Hu +3
Objectives Extraction of PICO (Populations, Interventions, Comparison, and Outcomes) entities is fundamental to evidence retrieval. We present a novel method PICOX to extract overl…
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…
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…