Classification of Adolescent Psychiatric Patients at High Risk of Suicide Using the Personality Assessment Inventory by Machine Learning

• 발행기관: 대한신경정신의학회
• 저자명: Kyung-Won Kim Jae Seok Lim Chan-Mo Yang Seung-Ho Jang Sang-Yeol Lee
• 간행물 정보: 『Psychiatry Investigation』제18권 제11호, 1137~1143쪽, 전체 7쪽
• 주제분류: 의약학 > 정신과학
• 파일형태: PDF
• 발행일자: 2021.11.30

영문 초록

Objective There are growing interests on suicide risk screening in clinical settings and classifying high-risk groups of suicide with suicidal ideation is crucial for a more effective suicide preventive intervention. Previous statistical techniques were limited because they tried to predict suicide risk using a simple algorithm. Machine learning differs from the traditional statistical techniques in that it generates the most optimal algorithm from various predictors. Methods We aim to analyze the Personality Assessment Inventory (PAI) profiles of child and adolescent patients who received outpatient psychiatric care using machine learning techniques, such as logistic regression (LR), random forest (RF), artificial neural network (ANN), support vector machine (SVM), and extreme gradient boosting (XGB), to develop and validate a classification model for individuals with high suicide risk. Results We developed prediction models using seven relevant features calculated by Boruta algorithm and subsequently tested all models using the testing dataset. The area under the ROC curve of these models were above 0.9 and the RF model exhibited the best performance. Conclusion Suicide must be assessed based on multiple aspects, and although Personality Assessment Inventory for Adolescent assess an array of domains, further research is needed for predicting high suicide risk groups.

목차

INTRODUCTION

METHODS

RESULTS

DISCUSSION

키워드

Suicide Child and adolescent psychiatry Machine learning Personality Assessment Inventory-Adolescent