[临床问卷鉴定职业心理健康障碍倾向的预测能力]
Enrique Escobar1, Juan Pablo Osorio2, Natalia Martini2
1Universidad Complutense de Madrid, Madrid, España.
概括
机器学习算法可以提高智利与工作有关的心理健康障碍的准确性和客观性. 改进的模型显示高精度,但需要数据平衡以获得更好的特异性.
科学领域:
- 职业健康 职业健康 职业健康 职业健康
- 机器学习应用程序 机器学习应用程序
- 心理障碍评估心理障碍的评估
背景情况:
- 智利的法律第. 16.744规定了与工作有关的精神健康障碍的资格,但在准确性和客观性方面面临挑战.
- 现有的资格认证过程可能缺乏一致和客观的标准.
研究的目的:
- 评估使用机器学习 (ML) 算法的可行性,以提高心理健康状况的资格.
- 评估ML在提高诊断过程的准确性和客观性的潜力.
主要方法:
- 对340名智利工人进行了心理健康疾病倾向调查问卷.
- 评估了决策树和后勤回归模型,包括增强版本,增加了额外的与压力因素相关的变量 ("Plus").
主要成果:
- "加树"模型表现出高准确度 (91.2%),特别是在平衡数据 (76.9%) 的情况下.
- 由于与精神健康状况的存在或不存在相关的数据不平衡,模型特异性需要改进.
结论:
- 整合ML算法可以显著提高心理健康障碍资格的效率和客观性.
- 扩大数据库和完善ML模型是实现卓越诊断精度的关键下一步.
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