简洁的机器学习预测模型中国自杀企图:基于人口和社会因素
Juncheng Lyu1, Chao Wang2, Zhe Gao1
1Shandong Second Medical University, School of Public Health, China.
Journal of affective disorders
|June 11, 2025
概括
机器学习模型有效预测中国人群中自杀企图 (SA). 随机森林 (RF) 模型显示了最高的整体准确性,这表明了改善临床应用的潜力.
科学领域:
- 计算精神病学是一种计算精神病学.
- 临床信息学 临床信息学
- 公共卫生研究 公共卫生研究
背景情况:
- 机器学习 (ML) 方法越来越多地用于自杀企图 (SA) 预测.
- 对中国人群的ML模型存在有限的研究,通常使用过于复杂的变量.
- 简洁和适用的预测模型的需求至关重要.
研究的目的:
- 探索ML方法在预测自杀企图中的有效性.
- 为SA开发一个更简洁,更适用的预测模型.
- 为了比较在中国队伍中多个ML算法的性能.
主要方法:
- 在中国的一项病例对照调查中,收集了人口统计数据,并使用了GSS自杀态度量表和自杀想法贝克量表.
- 使用了五种ML方法:随机森林 (RF),MLR,XGBoost,AdaBoost和LightGBM.
- 模型性能使用标准指数进行评估,R 4.2.1 软件协助分析.
主要成果:
- 多个ML模型实现了曲线下的面积 (AUC) 值大于0.75,表明了良好的预测效率.
- 射频模型在测试数据集中实现了最高的整体AUC (0.8638) 和最佳准确性 (79.09%).
- 在训练数据集中,LightGBM显示了最高的AUC (0.9199) 和最高的正预测值.
结论:
- 所有评估的ML算法在区分SA病例和非SA病例方面表现良好.
- 在这项研究中,RF和LightGBM成为了表现最好的模型.
- 未来的研究应该探索组合或组合模型策略,以进一步提高预测准确性和检测率.
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