机器学习技术在创伤后应激障碍中的应用:系统性审查和元分析
Jing Wang1,2, Hui Ouyang1,2, Runda Jiao3
1Lab for Post-traumatic Stress Disorder, Faculty of Psychology and Mental Health, Naval Medical University, 200433, Shanghai, China.
NPJ digital medicine
|May 9, 2024
概括
机器学习 (ML) 有效地分类创伤后应激障碍 (PTSD),多维数据产生更高的准确性. 这次审查强调了ML的ML.
科学领域:
- 精神病学和心理健康 精神病学和心理健康
- 数据科学和人工智能数据科学和人工智能
- 计算医学是一种计算医学.
背景情况:
- 创伤后应激障碍 (PTSD) 是一个重要的心理健康问题.
- 在PTSD中缺乏大数据和机器学习 (ML) 应用的全面审查.
- 现有的研究还没有综合ML在PTSD分类中的有效性.
研究的目的:
- 综合审查和分析大数据和ML技术在PTSD中的应用.
- 评估ML算法在PTSD分类中的性能.
- 为了比较单个与多维数据类型在PTSD分类中使用ML的有效性.
主要方法:
- 系统的文献搜索确定了873项相关研究.
- 对31项涉及210,001名参与者的样本大小的研究进行了定量分析.
- 在PTSD分类中对ML算法性能进行元分析.
主要成果:
- 机器学习算法在区分PTSD方面表现出很高的准确性 (整体准确率为0.89).
- 在使用多维数据 (0.96) 时,与单个数据类型 (0.86-0.90) 相比,聚合分类的准确性显著更高.
- 在PTSD分类中,ML技术是有效的,多模式方法的表现优于单模式方法.
结论:
- ML技术在PTSD的分类,识别,诊断和治疗方面显示出重大前景.
- 多维数据集成提高了在PTSD评估中ML模型的性能.
- 需要进一步的研究来优化ML算法和数据组合,以便在个人层面上进行临床应用.
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