This paper explores the development and application of an automated system designed to extract information from semi-structured interview transcripts. Given the labor-intensive nature of traditional qualitative analysis methods, such as coding, there exists a significant demand for tools that can facilitate the analysis process. Our research investigates various topic modeling techniques and concludes that the best model for analyzing interview texts is a combination of BERT embeddings and HDBSCAN clustering. We present a user-friendly software prototype that enables researchers, including those without programming skills, to efficiently process and visualize the thematic structure of interview data. This tool not only facilitates the initial stages of qualitative analysis but also offers insights into the interconnectedness of topics revealed, thereby enhancing the depth of qualitative analysis.
AnchorSIPS: A Synthetic Dataset and Evaluation Resource for Evidence-Supported Psychosis-Risk Symptom Measurement
Monash University（莫纳什大学）；Orygen（奥瑞金）；The University of Melbourne（墨尔本大学）；Swinburne University of Technology（斯威本科技大学）；University of Liverpool（利物浦大学）；Khalifa University（哈利法大学）
DialogPII: A multilingual dataset of synthetic dialog transcripts to detect personal information
German Research Center for Artificial Intelligence (DFKI)（德国人工智能研究中心（DFKI））；Technical University Berlin（柏林技术大学）；Berlin Institute of Health (BIH)（柏林健康研究所（BIH））；Tilburg University（蒂尔堡大学）；Universidad de Buenos Aires（布宜诺斯艾利斯大学）；Independent Researcher（独立研究者）；Bundesinstitut für Risikobewertung (BfR)（风险评估联邦机构（BfR））
Analyzing Cancer Patients' Experiences with Embedding-based Topic Modeling and LLMs
Leiden Institute of Advanced Computer Science (LIACS), Leiden, The Netherlands（莱顿高级计算机科学研究所（LIACS），莱顿，荷兰）；Leiden University Medical Center (LUMC), Leiden, The Netherlands（莱顿大学医学中心（LUMC），莱顿，荷兰）
TAMA: A Human-AI Collaborative Thematic Analysis Framework Using Multi-Agent LLMs for Clinical Interviews
School of Information, University of Texas at Austin（信息学院，德克萨斯大学奥斯汀分校）；Department of Biomedical Engineering, University of Texas at Austin（生物医学工程系，德克萨斯大学奥斯汀分校）；College of Natural Sciences, University of Texas at Austin（自然科学院，德克萨斯大学奥斯汀分校）；Graphen, Inc.（Graphen公司）；Department of Cardiac Surgery, Division of Pediatric Cardiac Surgery, Vanderbilt University School of Medicine（心脏外科系，范德比尔特大学医学中心）；Pediatric Heart Institute, Monroe Carell Jr. Children’s Hospital at Vanderbilt（儿童心脏研究所，范德比尔特儿童医院）；Department of Computer Science, University of Virginia（计算机科学系，弗吉尼亚大学）；Department of Computer Science, University of Illinois at Urbana-Champaign（计算机科学系，伊利诺伊大学厄巴纳-香槟分校）；Department of Computer Science, University of Texas at Austin（计算机科学系，德克萨斯大学奥斯汀分校）；McCombs School of Business, University of Texas at Austin（麦克阿瑟商学院，德克萨斯大学奥斯汀分校）；Dell Medical School, University of Texas at Austin（德克萨斯大学奥斯汀分校德莱尔医学院）
DECT: Harnessing LLM-assisted Fine-Grained Linguistic Knowledge and Label-Switched and Label-Preserved Data Generation for Diagnosis of Alzheimer's Disease
The University of Hong Kong（香港大学）；The Chinese University of Hong Kong（香港中文大学）
