Development and Evaluation of the DMIND Questionnaire: Preparing for AI Integration into an Effective Depression Screening Tool
Issued Date
2024-01-01
Resource Type
eISSN
22288082
Scopus ID
2-s2.0-85204118145
Journal Title
Siriraj Medical Journal
Volume
76
Issue
9
Start Page
620
End Page
629
Rights Holder(s)
SCOPUS
Bibliographic Citation
Siriraj Medical Journal Vol.76 No.9 (2024) , 620-629
Suggested Citation
Hemrungrojn S., Saengsai K., Jakkrawankul P., Kiattiporn-Opas C., Chaichareenon K., Amrapala A., Lapanan K., Hengpraprom S., Hiransuthikul N., Achakulvisut T., Nupairoj N., Phutrakool P., Yodlorchai R., Vateekul P. Development and Evaluation of the DMIND Questionnaire: Preparing for AI Integration into an Effective Depression Screening Tool. Siriraj Medical Journal Vol.76 No.9 (2024) , 620-629. 629. doi:10.33192/smj.v76i9.269527 Retrieved from: https://repository.li.mahidol.ac.th/handle/20.500.14594/101310
Title
Development and Evaluation of the DMIND Questionnaire: Preparing for AI Integration into an Effective Depression Screening Tool
Corresponding Author(s)
Other Contributor(s)
Abstract
Objective: Thailand’s mental health crisis is exacerbated by high demand and a shortage of mental health professionals. The research objective was to develop and validate the Detection and Monitoring Intelligence Network for Depression (DMIND) questionnaire, designed to be culturally relevant and easily administered in clinical settings. Crafted with expert input, items specifically conducive to artificial intelligence (AI) analysis were selected to facilitate the future development of an AI-assisted depression scoring model. This approach underscores the tool’s dual utility in both human-led and technology-enhanced diagnostics. Materials and Methods: We enrolled 81 participants from psychiatric and tertiary care hospitals in Bangkok. Participants were assessed using the DMIND questionnaire, followed by the Hamilton Depression Rating Scale (HDRS-17). Statistical analyses included the content validity index (CVI), Cronbach’s alpha, Pearson’s correlation coefficient, Cohen’s kappa, and receiver operating characteristic (ROC) analysis. The Liu method, Youden index, and nearest neighbor method were used to determine the optimal cut-off point Results: The DMIND questionnaire showed strong validity, with an item-level CVI (I-CVI) and scale-level CVI (S-CVI) exceeding 1.0, indicating strong consensus on its relevance and utility. The tool also demonstrated high internal consistency (Cronbach’s alpha = 0.96). ROC analysis showed an AUC of 0.88, indicating high accuracy in depression screening. An optimal cut-off score of 11.5 was identified, balancing predictive value and sensitivity. Conclusion: The DMIND questionnaire represents a significant advancement in innovative mental health diagnostics, addressing unmet clinical needs by providing accurate and efficient assessments capable of AI integration for further enhancing mental health service delivery in Thailand.