在PTSD研究中对机器学习进行系统审查,用于自动化诊断评估
Yuqi Wu1, Kaining Mao1, Liz Dennett2
1Electrical & Computer Engineering Department, Faculty of Engineering, University of Alberta, Edmonton, AB, Canada.
Npj mental health research
|April 12, 2024
概括
机器学习 (ML) 对诊断创伤后应激障碍 (PTSD) 有希望,克服诊断挑战. 需要进一步的研究来解决AI在PTSD诊断中的临床实施的伦理和监管问题.
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
- 心理健康诊断 心理健康诊断 心理健康诊断
背景情况:
- 创伤后应激障碍 (PTSD) 由于其复杂的性质和可访问性障碍,往往被诊断不足.
- 现有的诊断方法在准确及时识别PTSD方面面临挑战.
- 机器学习 (ML) 为心理健康的早期评估和预测提供了潜在的解决方案.
研究的目的:
- 系统地审查有关ML在PTSD诊断中的实用性的文献.
- 评估人工智能 (AI) 作为PTSD诊断工具的潜力.
- 提供关于实施ML模型用于PTSD诊断的指导.
主要方法:
- 对3186项研究的系统综述,其中41篇文章被纳入最终综合.
- 使用统计方法来综合结果并指导ML实施.
- 考虑因素包括ML模型选择,特征识别,样本大小确定和验证工具.
主要成果:
- 纳入研究的分析表明,人工智能对PTSD诊断具有显著的潜力.
- 审查综合了成功实施这一领域的ML任务的关键因素.
- 有证据表明,ML可以帮助克服PTSD的诊断异质性和可访问性问题.
结论:
- 人工智能作为改善创伤后应激障碍诊断的工具,显示出相当大的希望.
- 基于人工智能的诊断系统的临床实施需要仔细考虑监管,伦理和隐私方面的问题.
- 需要进一步的开发和验证,以有效地将AI整合到PTSD的现实世界临床环境中.
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