区分创伤后应激障碍的机器学习方法:系统审查和元分析的协议
Jing Wang1,2, Hui Ouyang1,2, Runda Jiao3
1Lab for Post-traumatic Stress Disorder, Faculty of Psychology and Mental Health, Naval Medical University, Shanghai, China.
Digital health
|March 18, 2024
概括
机器学习 (ML) 有助于使用各种数据识别创伤后应激障碍 (PTSD). 本综述评估了用于PTSD分类的ML研究,为其诊断潜力提供了见解.
科学领域:
- 心理健康 心理健康
- 计算精神病学是一种计算精神病学.
- 数据科学数据科学数据科学
背景情况:
- 公共心理健康受到威胁,创伤后应激障碍 (PTSD) 是一个重大问题,由COVID-19大流行加剧.
- 机器学习 (ML) 技术在心理健康研究中越来越多地用于诊断和分类目的.
研究的目的:
- 对现有研究进行全面评估,这些研究采用了ML方法来对患有PTSD的个体进行分类.
- 通过ML分析使用各种数据模式进行PTSD分类的研究.
主要方法:
- 从2000年1月到2022年5月,在英语和中文数据库中发表的研究的系统审查和元分析.
- 由两名研究人员进行独立选和数据提取,并使用Ramos-Lima (2020) 的框架进行质量评估.
- 统计分析包括效果大小计算 (率,标准误差,95%CI),异质性评估 (科克兰的Q测试) 和偏差检测 (道图,埃格尔的测试).
主要成果:
- 在审查完成后,该部分将被填写.
- 该审查将综合选定关于基于ML的PTSD分类研究的研究结果.
- 将分析异质性和出版偏见,以确保可靠的结论.
结论:
- 这一系统性审查和元分析将为PTSD分类中的ML应用提供一个综合的概述.
- 这些发现将为未来的研究方向和ML在PTSD诊断中的潜在临床应用提供信息.
- 这项研究已注册为PROSPERO (CRD42023342042).
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