Haghish E F, Czajkowski Nikolai O, von Soest Tilmann
Department of Psychology, Faculty of Social Sciences, University of Oslo, Oslo, Norway.
Department of Mental Disorders, Division of Mental and Physical Health, Norwegian Institute of Public Health (NIPH), Oslo, Norway.
Front Psychiatry. 2023 Sep 26;14:1216791. doi: 10.3389/fpsyt.2023.1216791. eCollection 2023.
Research on the classification models of suicide attempts has predominantly depended on the collection of sensitive data related to suicide. Gathering this type of information at the population level can be challenging, especially when it pertains to adolescents. We addressed two main objectives: (1) the feasibility of classifying adolescents at high risk of attempting suicide without relying on specific suicide-related survey items such as history of suicide attempts, suicide plan, or suicide ideation, and (2) identifying the most important predictors of suicide attempts among adolescents.
Nationwide survey data from 173,664 Norwegian adolescents (ages 13-18) were utilized to train a binary classification model, using 169 questionnaire items. The Extreme Gradient Boosting (XGBoost) algorithm was fine-tuned to classify adolescent suicide attempts, and the most important predictors were identified.
XGBoost achieved a sensitivity of 77% with a specificity of 90%, and an AUC of 92.1% and an AUPRC of 47.1%. A coherent set of predictors in the domains of internalizing problems, substance use, interpersonal relationships, and victimization were pinpointed as the most important items related to recent suicide attempts.
This study underscores the potential of machine learning for screening adolescent suicide attempts on a population scale without requiring sensitive suicide-related survey items. Future research investigating the etiology of suicidal behavior may direct particular attention to internalizing problems, interpersonal relationships, victimization, and substance use.
自杀未遂分类模型的研究主要依赖于收集与自杀相关的敏感数据。在人群层面收集这类信息可能具有挑战性，尤其是涉及青少年时。我们解决了两个主要目标：（1）在不依赖自杀未遂史、自杀计划或自杀意念等特定自杀相关调查项目的情况下，对有自杀未遂高风险的青少年进行分类的可行性，以及（2）确定青少年自杀未遂的最重要预测因素。
利用来自173,664名挪威青少年（13 - 18岁）的全国性调查数据，使用169个问卷项目训练一个二元分类模型。对极端梯度提升（XGBoost）算法进行微调以对青少年自杀未遂进行分类，并确定最重要的预测因素。
XGBoost实现了77%的灵敏度、90%的特异度、92.1%的曲线下面积（AUC）和47.1%的精确率-召回率曲线下面积（AUPRC）。在内化问题、物质使用、人际关系和受侵害等领域的一组连贯预测因素被确定为与近期自杀未遂相关的最重要项目。
本研究强调了机器学习在不要求敏感自杀相关调查项目的情况下在人群规模上筛查青少年自杀未遂的潜力。未来研究自杀行为病因学的研究可能会特别关注内化问题、人际关系、受侵害和物质使用。
