Simultaneous Utilization of Mood Disorder Questionnaire and Bipolar Spectrum Diagnostic Scale for Machine Learning-Based Classification of Patients With Bipolar Disorders and Depressive Disorders

• 발행기관: 대한신경정신의학회
• 저자명: Kyungwon Kim
• 간행물 정보: 『Psychiatry Investigation』제21권 제8호, 877~884쪽, 전체 8쪽
• 주제분류: 의약학 > 정신과학
• 파일형태: PDF
• 발행일자: 2024.08.31

국문 초록

Objective Bipolar and depressive disorders are distinct disorders with clearly different clinical courses, however, distinguishing between them often presents clinical challenges. This study investigates the utility of self-report questionnaires, the Mood Disorder Questionnaire (MDQ) and Bipolar Spectrum Diagnostic Scale (BSDS), with machine learning-based multivariate analysis, to classify patients with bipo-lar and depressive disorders. Methods A total of 189 patients with bipolar disorders and depressive disorders were included in the study, and all participants complet-ed both the MDQ and BSDS questionnaires. Machine-learning classifiers, including support vector machine (SVM) and linear discrimi-nant analysis (LDA), were exploited for multivariate analysis. Classification performance was assessed through cross-validation. Results Both MDQ and BSDS demonstrated significant differences in each item and total scores between the two groups. Machine learning-based multivariate analysis, including SVM, achieved excellent discrimination levels with area under the ROC curve (AUC) values exceeding 0.8 for each questionnaire individually. In particular, the combination of MDQ and BSDS further improved classifica-tion performance, yielding an AUC of 0.8762. Conclusion This study suggests the application of machine learning to MDQ and BSDS can assist in distinguishing between bipolar and depressive disorders. The potential of combining high-dimensional psychiatric data with machine learning-based multivariate analysis as an effective approach to psychiatric disorders.

목차

INTRODUCTION

METHODS

RESULTS

DISCUSSION

키워드

Bipolar disorder Depression Machine learning Classification Self report