Explainable Artificial Intelligence for Mental Disorder Screening: A Computational Design Science Approach

• 成果类型: Article
• 署名作者: Tutun, Salih; Topuz, Kazim; Tosyali, Ali; Bhattacherjee, Anol; Li, Gorden
• 署名单位: Washington University (WUSTL); University of Tulsa; Rochester Institute of Technology; State University System of Florida; University of South Florida
• 刊物名称: JOURNAL OF MANAGEMENT INFORMATION SYSTEMS
• ISSN/ISSBN: 0742-1222
• DOI: 10.1080/07421222.2024.2415771
• 发表日期: 2024
• 页码: 958-981
• 关键词: decision-support-system; Social media

摘要

Mental disorders affect nearly one billion people globally, 94% of whom are undiagnosed and untreated due to an acute shortage of trained clinicians. In response to this crisis, this study introduces mental disorder scan (MDscan), a novel artifact for screening ten mental disorders using data from the SCL-90-R mental disorder screening instrument, an explainable artificial intelligence approach, and our own ShapRadiation algorithm. MDscan converts 90 mental health indicators for each patient into an easily interpretable diagnostic image for mental disorders, similar to radiological images, and explains which indicators contributed to that prediction, increasing clinicians' ability to screen more patients in less time. A field evaluation with clinical data shows that MDscan has high classification accuracy, with average F1 scores between 0.77 and 0.94, compared against prerecorded ground truth. Furthermore, unlike traditional black-box models, MDscan's transparency and explainability can help enhance trust in artificial intelligence (AI) applications for clinical use.