大学学生群体中潜在的自杀风险:用于识别预测因素和不同学生的风险概况的机器学习方法
Jessica Dagani1, Chiara Buizza2, Clarissa Ferrari3
1Department of Clinical and Experimental Sciences, Section of Clinical and Dynamic Psychology, University of Brescia, Viale Europa, 11, 25123, Brescia, Italy. jessica.dagani@gmail.com.
概括
机器学习确定了大学生自杀风险的关键预测因素. 心理困扰和吸毒是显著的危险因素,而宗教实践可能有保护作用.
科学领域:
- 精神病学是一个精神病学.
- 机器学习 机器学习
- 公共卫生 公共卫生
背景情况:
- 自杀是大学生死亡的主要原因.
- 社会人口,关系和临床因素预测了自杀风险.
- 机器学习提供先进的自杀风险评估方法.
研究的目的:
- 确定大学生自杀风险的预测因素.
- 定义与自杀风险相关的学生个人资料.
- 使用机器学习方法进行风险评估.
主要方法:
- 在3102名学生的横截面观测研究中.
- 调查学生自杀风险,人口统计,学术和健康状况.
- 应用分类树和多重对应分析.
主要成果:
- 7%的学生表现出高自杀风险; 3.8%的学生有自杀尝试史.
- 心理困扰和酒精/物质使用是高自杀风险的关键预测因素.
- 低压力,低使用物质和宗教实践的特点是自杀风险较低.
结论:
- 机器学习显示了评估学生自杀风险的潜力.
- 预防计划应解决风险和保护因素.
- 针对不同风险类别需要量身定制的干预措施.
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