使用机器学习方法来预测创伤后应激障碍心理治疗的结果:一个系统的审查
James Tait1, Stephen Kellett2, Jaime Delgadillo3
1School of Psychology, University of Sheffield, ICOSS Building, 219 Portobello, Sheffield S1 4DP, United Kingdom; Department of Health Sciences, University of York, Seebohm Rowntree Building, Heslington, York YO10 5DD, United Kingdom.
Journal of anxiety disorders
|March 25, 2025
概括
机器学习 (ML) 显示了预测创伤后应激障碍 (PTSD) 治疗结果的前景. 然而,目前的研究要求更好地遵守ML最佳实践,以获得可靠的精确心理健康护理.
科学领域:
- 精神病学和心理健康 精神病学和心理健康
- 计算科学 计算科学
- 临床心理学 临床心理学
背景情况:
- 创伤后应激障碍 (PTSD) 缺乏数据驱动的治疗选择方法.
- 机器学习 (ML) 提供了预测治疗结果的潜力,并使精确的精神卫生保健成为可能.
研究的目的:
- 系统地审查使用ML的研究,以预测成人PTSD心理治疗结果.
- 评估这些ML应用程序的方法严谨性.
主要方法:
- 进行了临床预测研究的预先注册系统审查.
- 使用PROBAST工具评估偏差风险.
- 叙述综合研究方法,发现和ML最佳实践的坚持.
主要成果:
- 十七项研究符合纳入标准,使用各种ML方法 (监督和无监督).
- 所有研究都显示出偏差的高风险,主要是由于样本规模小,缺乏功率计算.
- 发现ML方法 (例如,超参数调整,交叉验证) 的报告不一致,外部验证有限.
结论:
- 机器学习有可能促进创伤后应激障碍的精确心理治疗.
- 更多地遵守ML最佳实践指南对于实现这一潜力至关重要.
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