AI RESEARCH
Analyzing Cancer Patients' Experiences with Embedding-based Topic Modeling and LLMs
arXiv CS.CL
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ArXi:2601.12154v2 Announce Type: replace This study investigates the use of neural topic modeling and LLMs to uncover meaningful themes from patient storytelling data, to offer insights that could contribute to patient-oriented healthcare practices. We analyze a collection of transcribed interviews with cancer patients (132,722 words in 13 interviews). We first evaluate BERTopic and Top2Vec for individual interview summarization by using similar preprocessing, chunking, and clustering configurations to ensure a fair comparison on Keyword Extraction.