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Conference Paper
A Study on the Construction and Utility Analysis of a Dataset of Interviews with Korean Disaster Survivors
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- Authors
- Dong Hoon Son, Seung Hun Oh, Hong Yeon Yu, Sim-Kwon Yoon, Jeong Eun Kim
- Issue Date
- 2024-12
- Citation
- International Conference on Bioinformatics and Biomedicine (BIBM) 2024, pp.7136-7138
- Language
- English
- Type
- Conference Paper
- Abstract
- Post-Traumatic Stress Disorder (PTSD) is characterized by various maladaptive responses that occur after experiencing or witnessing severe trauma, underscoring the importance of early detection and the development of automated diagnostic systems. This study aimed to construct the Korean Disaster Survivors Interview (KDSI) dataset and use it to develop and evaluate an AI-based diagnostic model for PTSD. We designed and trained four deep learning models for the classification of PTSD, anxiety, and depression using the KDSI dataset, with acoustic features extracted for performance analysis. The results demonstrated that the ensemble model (Model D) achieved the highest F1 score and AUC in anxiety classification and also showed strong performance in PTSD and depression classifications. Despite the potential noise and reliability issues associated with self-reported data collected online, the findings confirm that the KDSI dataset is a valuable resource for building AI models to assess disaster-related psychological conditions, including PTSD, anxiety, and depression.
- KSP Keywords
- Data collected, Early detection, Performance analysis, Post-traumatic stress disorder, Severe trauma, Utility analysis, acoustic features, deep learning(DL), deep learning models, diagnostic model, diagnostic system