机器学习,深度学习和数据预处理技术用于检测,预测和监测压力和与压力相关的精神障碍:范围审查
Moein Razavi1,2, Samira Ziyadidegan1, Ahmadreza Mahmoudzadeh3
1Department of Industrial and Systems Engineering, Texas A&M University, College Station, TX, United States.
机器学习 (ML) 模型,特别是支持矢量机器,神经网络和随机森林,在检测和预测心理压力和相关疾病方面表现有前途. 物理数据和维度减小是这些进步的关键.
科学领域:
- 计算精神病学是一种计算精神病学.
- 医疗保健中的人工智能
背景情况:
- 心理压力和心理健康障碍 (MDs) 是一个重要的公共卫生问题.
- 机器学习 (ML) 为理解和管理心理压力和MDs提供计算方法.
研究的目的:
- 审查用于检测,预测和分析心理压力和MDs的ML方法.
- 确定ML在这个领域的当前趋势和应用.
主要方法:
- 根据PRISMA-ScR指南进行严格的范围审查.
- 在压力研究中检查ML算法,预处理技术和数据类型.
主要成果:
- 支持矢量机器,神经网络和随机森林模型表现出高精度和稳定性.
- 生理学参数 (心率,皮肤反应) 是常见的预测因素.
- 尺寸缩小技术是关键的预处理步骤.
结论:
- 确定的研究差距包括模型解释性,个性化和实时处理.
- 未来的方向强调自然主义的设置和增强的预测能力.
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