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基于机器学习算法对青少年自杀和自伤行为的预测模型的研究

Yao Gan¹, Li Kuang¹, Xiao-Ming Xu¹

¹Department of Psychiatry, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China.

Frontiers in psychiatry

|March 21, 2025

概括

这项研究确定了青少年自杀和自我伤害行为的关键风险因素,并开发了一种用于早期检测的机器学习模型. 后勤回归模型在预测这些行为方面表现出很高的准确性,使得有针对性的干预措施成为可能.

科学领域:

精神病学是一个精神病学.
机器学习机器学习
青少年健康青少年健康

背景情况:

青少年的自杀和自我伤害行为是严重的公共卫生问题.
识别风险因素和开发预测模型对于预防和干预至关重要.

研究的目的:

探索与青少年自杀和自我伤害行为相关的风险因素.
为这些行为构建基于机器学习的预测模型.

主要方法:

在重庆3000名高中生中进行了分层集群抽样.
后勤回归分析以确定独立的风险因素.
用于预测建模的六种机器学习算法 (MLP,RF,KNN,SVM,LR,XGBoost) 的比较.

主要成果:

性别,冲动性,精神病,神经病,人际关系敏感性,抑郁症和偏执症被确定为独立的危险因素.
后勤回归模型实现了最高的灵敏度 (0.9948) 和特异性 (0.9981).
随机森林,多级感知子和极端梯度增强模型显示了可接受的性能.

结论:

冲动性,精神病症,神经病症,人际关系敏感性,抑郁症和偏执症的青少年有更高的风险.
机器学习模型有效地分类和预测青少年自杀和自我伤害风险.
早期识别使得有针对性的干预措施能够缓解这些行为.

关键词:

青少年青少年青少年机器学习算法机器学习算法预测模型预测模型有关风险因素的风险因素.自杀和自我伤害行为.

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