6 citations · 6 across the 3 of their papers we have counts for
5 papers · 1 filter
Retrieval augmented generation based dynamic prompting for few-shot biomedical named entity recognition using large language models
Yao Ge, Sudeshna Das, Yuting Guo +1
Biomedical named entity recognition (NER) is a high-utility natural language processing (NLP) task, and large language models (LLMs) show promise particularly in few-shot settings…
Leveraging large language models and traditional machine learning ensembles for ADHD detection from narrative transcripts
Yuxin Zhu, Yuting Guo, Noah Marchuck +2
Despite rapid advances in large language models (LLMs), their integration with traditional supervised machine learning (ML) techniques that have proven applicability to medical dat…
HILGEN: Hierarchically-Informed Data Generation for Biomedical NER Using Knowledgebases and Large Language Models
Yao Ge, Yuting Guo, Sudeshna Das +3
We present HILGEN, a Hierarchically-Informed Data Generation approach that combines domain knowledge from the Unified Medical Language System (UMLS) with synthetic data generated b…
Benchmarking Open-Source Large Language Models on Healthcare Text Classification Tasks
Yuting Guo, Abeed Sarker
The application of large language models (LLMs) to healthcare information extraction has emerged as a promising approach. This study evaluates the classification performance of fiv…
Few-shot learning for medical text: A systematic review
Yao Ge, Yuting Guo, Yuan-Chi Yang +2
Objective: Few-shot learning (FSL) methods require small numbers of labeled instances for training. As many medical topics have limited annotated textual data in practical settings…