5 papers · 1 filter
Automated Thematic Analysis for Clinical Qualitative Data: Iterative Codebook Refinement with Full Provenance
Seungjun Yi, Joakim Nguyen, Huimin Xu +9
Thematic analysis (TA) is widely used in health research to extract patterns from patient interviews, yet manual TA faces challenges in scalability and reproducibility. LLM-based a…
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…
ProtoMed-LLM: An Automatic Evaluation Framework for Large Language Models in Medical Protocol Formulation
Seungjun Yi, Jaeyoung Lim, Juyong Yoon
Automated generation of scientific protocols executable by robots can significantly accelerate scientific research processes. Large Language Models (LLMs) excel at Scientific Proto…