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자가보고 설문 데이터 기반 정신건강 검진도구 개발 예비 연구: 전반적 기능 상태와의 연관성을 중심으로

A Preliminary Study for the Development of Data-Driven Screening Tool for Mental Health Using Self-Report Questionnaires Reflecting General Functional Status

권준수|김민아|김현식|최우리|황우정
대한신경정신의학회 (Korean Neuropsychiatric Association)|2022.11|신경정신의학 (JOURNAL OF THE KOREAN NEUROPSYCHIATRIC ASSOCIATION)|제61권 제4호|pp. 297-307|DOI https://doi.org/10.4306/jknpa.2022.61.4.297

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키워드

Machine learningMental healthScreening toolSelf-reportFunctioning.
주제분류정신과학

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.

Abstract

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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