307 citations
- The University of Texas Health Science CenterUS14 papers
- The University of Texas MD Anderson Cancer CenterUS14 papers
- Yale UniversityUS11 papers
- Rice UniversityUS8 papers
- Mayo Clinic in FloridaUS7 papers
- Harvard UniversityUS6 papers
- University of PennsylvaniaUS5 papers
- Cornell UniversityUS4 papers
- National Institutes of HealthUS4 papers
- Boston Children's HospitalUS3 papers
- Fudan UniversityCN3 papers
- Heidelberg UniversityDE3 papers
95 papers
Clinical Document Metadata Extraction: A Scoping Review
Kurt Miller, Qiuhao Lu, William Hersh +4
Clinical document metadata, such as document type, structure, author role, medical specialty, and encounter setting, is essential for accurate interpretation of information capture…
Bayesian Geostatistical Modeling for Cluster Randomized Trials
Jooyeon Lee, M. S., Evan Kwiatkowski +1
Cluster randomized trials (CRTs) offer a practical alternative for addressing logistical challenges and ensuring feasibility in community health, education, and prevention studies,…
Memorization in Large Language Models in Medicine: Prevalence, Characteristics, and Implications
Anran Li, Lingfei Qian, Mengmeng Du +18
Large Language Models (LLMs) have demonstrated significant potential in medicine, with many studies adapting them through continued pre-training or fine-tuning on medical data to e…
Synthesized Annotation Guidelines are Knowledge-Lite Boosters for Clinical Information Extraction
Enshuo Hsu, Martin Ugbala, Krishna Kumar Kookal +4
Generative information extraction using large language models, particularly through few-shot learning, has become a popular method. Recent studies indicate that providing a detaile…
KMT2B-related disorders: expansion of the phenotypic spectrum and long-term efficacy of deep brain stimulation
L Cif, D Demailly, JP Lin +112
Heterozygous mutations in KMT2B are associated with an early-onset, progressive, and often complex dystonia (DYT28). Key characteristics of typical disease include focal motor feat…
LLM-IE: A Python Package for Generative Information Extraction with Large Language Models
Enshuo Hsu, Kirk Roberts
Objectives: Despite the recent adoption of large language models (LLMs) for biomedical information extraction, challenges in prompt engineering and algorithms persist, with no dedi…