在指数创伤后进行密集的纵向评估,以利用机器学习预测PTSD的发展
Adam Horwitz1, Kaitlyn McCarthy1, Stacey L House2
1Department of Psychiatry, University of Michigan, Ann Arbor, MI 48109, USA.
Journal of anxiety disorders
|May 9, 2024
概括
一个简短的,每周对神经紧张,重复和疲劳的调查可以预测创伤后应激障碍 (PTSD) 风险在遭受创伤的成年人. 这种三项评估有助于早期识别及时干预.
科学领域:
- 创伤和紧急医疗医学
- 精神病学和心理健康 精神病学和心理健康
- 医疗保健中的机器学习
背景情况:
- 在遭受创伤后,识别患有创伤后应激障碍 (PTSD) 风险的个体存在重大挑战.
- 及时干预对于有患PTSD风险的人来说至关重要,需要有效的查策略.
研究的目的:
- 确定一组最小的创伤相关症状,评估创伤后早期,可以准确预测PTSD发展.
- 开发一种基于机器学习的务实预测算法,用于识别患有PTSD风险的个体.
主要方法:
- 一组2185名成年人参加了创伤暴露后的紧急护理.
- 参与者完成了一项"闪光调查",在八周内每周多次评估6至8个创伤症状.
- 在CART机器学习分析中,利用症状特征 (平均值,SD值,最后值,最差值,高峰值) 来构建一个预测模型.
主要成果:
- 创伤后应激障碍 (PTSD),以PCL-5 ≥38来定义,在8周的随访中,31% (669名参与者) 被诊断为患有创伤后应激障碍.
- 一个使用神经紧张,重复和疲劳的平均分数的分类树实现了曲线下的面积为0.836.
- 这项3项评估方案显示了对PTSD的高预测准确性.
结论:
- 一个简短的,每周3项评估协议 (紧张,重复,疲劳) 是可行的预测创伤后创伤后应激障碍风险.
- 这种方法可以促进早期识别和随访护理的个人有发展后应激障碍的风险.
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