预测自杀行为:一个机器学习模型
A-M Vejnović1, V Tatalović, M Vujović
1Department of Psychiatry and Psychological Medicine, Faculty of Medicine, University of Novi Sad, Novi Sad, Serbia. ana-marija.vejnovic@mf.uns.ac.rs.
European review for medical and pharmacological sciences
|December 4, 2025
概括
机器学习模型可以通过分析患者数据来预测自杀企图. 一个k-最近邻居 (kNN) 模型实现了87%的准确性,有助于早期风险识别和自杀预防工作.
科学领域:
- 精神病学是一个精神病学.
- 计算医学是一种计算医学.
- 公共卫生 公共卫生
背景情况:
- 及时识别自杀风险因素对于有效的干预和预防至关重要.
- 机器学习 (ML) 提供了一种强大的方法来分析临床数据,用于开发预测模型.
- 将患者分为高风险和低风险组可以显著帮助预防自杀的策略.
研究的目的:
- 开发和评估一种机器学习模型,根据患者自杀企图的风险对患者进行分类.
- 使用ML方法分析18个观察到的特征对自杀行为发展的影响.
- 创建一个预测工具,将个人分为自杀企图的高风险和低风险类别.
主要方法:
- 对301名住院精神病患者的临床数据进行分析,分为自杀行为和非自杀行为组.
- 应用机器学习方法来识别影响自杀行为的关键特征.
- 使用k-最近邻近 (kNN) 算法开发和训练一个预测模型.
主要成果:
- 基于kNN的模型显示,预测自杀风险的分类准确率为87%.
- 该模型在测试样本上获得了87%的灵敏度,90%的精度和85%的F分数.
- 通过ML分析确定了影响自杀行为的关键特征.
结论:
- 早期识别自杀行为风险因素对于有效预防自杀至关重要.
- 机器学习分类模型可以作为评估自杀风险的有价值的临床工具.
- 扩大数据集可以提高分类器的性能,并促进其融入临床实践,以减少自杀率.
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