3 citations · 4 across the 6 of their papers we have counts for
5 papers · 1 filter
Position: Thematic Analysis of Unstructured Clinical Transcripts with Large Language Models
Seungjun Yi, Joakim Nguyen, Terence Lim +8
This position paper examines how large language models (LLMs) can support thematic analysis of unstructured clinical transcripts, a widely used but resource-intensive method for un…
SFT-TA: Supervised Fine-Tuned Agents in Multi-Agent LLMs for Automated Inductive Thematic Analysis
Seungjun Yi, Joakim Nguyen, Huimin Xu +8
Thematic Analysis (TA) is a widely used qualitative method that provides a structured yet flexible framework for identifying and reporting patterns in clinical interview transcript…
Auto-TA: Towards Scalable Automated Thematic Analysis (TA) via Multi-Agent Large Language Models with Reinforcement Learning
Seungjun Yi, Joakim Nguyen, Huimin Xu +4
Congenital heart disease (CHD) presents complex, lifelong challenges often underrepresented in traditional clinical metrics. While unstructured narratives offer rich insights into…
TAMA: A Human-AI Collaborative Thematic Analysis Framework Using Multi-Agent LLMs for Clinical Interviews
Huimin Xu, Seungjun Yi, Terence Lim +9
Thematic analysis (TA) is a widely used qualitative approach for uncovering latent meanings in unstructured text data. TA provides valuable insights in healthcare but is resource-i…
LLM-TA: An LLM-Enhanced Thematic Analysis Pipeline for Transcripts from Parents of Children with Congenital Heart Disease
Muhammad Zain Raza, Jiawei Xu, Terence Lim +4
Thematic Analysis (TA) is a fundamental method in healthcare research for analyzing transcript data, but it is resource-intensive and difficult to scale for large, complex datasets…