자가보고 설문 데이터 기반 정신건강 검진도구 개발 예비 연구

• 영문명: A Preliminary Study for the Development of Data-Driven Screening Tool for Mental Health Using Self-Report Questionnaires Reflecting General Functional Status
• 발행기관: 대한신경정신의학회
• 저자명: 최우리 황우정 김현식 김민아 권준수
• 간행물 정보: 『신경정신의학』제61권 제4호, 297~307쪽, 전체 11쪽
• 주제분류: 의약학 > 정신과학
• 파일형태: PDF
• 발행일자: 2022.11.30

영문 초록

Objectives Mental health is not only closely related to daily functioning but also affects adaptability to issues in life. To date, however, there have been no simple and widely available screening tools that assess general mental health. Therefore, we sought to conduct a preliminary study for the development of a general mental health screening tool that can easily detect mental health problems at home. Methods We administered nine self-report questionnaires and the Global Assessment of Functioning (GAF) scale to 143 patients with psychosis, a typical mental illness, 125 subjects with a clinical high-risk for psychosis, a representative group with non-specific psychiatric symptoms such as depression or anxiety, and 118 healthy subjects as controls. A machine learning approach was used to identify a set of items that optimally predicted the GAF scores. A model was trained using the random forest algorithm, and the set limit was measured through ABC analysis to select the final set of questionnaire items. Results The results showed that 12 items from four questionnaires were included in the function prediction model; the Positive and Negative Affect Scale 1, 8, 17, 19, the Beck Depression Inventory 6, 14, the Symptom Checklist 3, 5, 15, 20, and the Affect Intensity Measure 39. The average mean squared error, which is representative of model performance, was 879.07. Conclusion These results suggest that these 12 self-report items could comprise a simple and easy mental health screening tool. We propose to conduct a follow-up study on a large general population to validate the clinical usefulness of this preliminary screening tool.

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키워드

Mental health Screening tool Machine learning Self-report Functioning