哪个患有泛性焦虑症的客户可以从正念生态瞬间干预中获益,而不是自监测应用程序? 开发一个多变量机器学习预测模型
Nur Hani Zainal1, Michelle G Newman2
1Harvard Medical School, Boston, MA, USA; National University of Singapore, Kent Ridge, Singapore.
Journal of anxiety disorders
|January 21, 2024
概括
机器学习模型可以个性化通用焦虑症 (GAD) 治疗. 特定的患者特征预测了正念干预措施与自我监测应用程序的更好的结果.
科学领域:
- 精神病学是一个精神病学.
- 计算精神病学是一种计算精神病学.
- 数字健康数字健康
背景情况:
- 通用焦虑症 (GAD) 是一种普遍的疾病,需要个性化治疗方法.
- 正念生态瞬间干预 (MEMI) 和自我监控应用 (SM) 是GAD管理的数字工具.
- 确定哪些患者从特定干预中获益最多仍然是一个挑战.
研究的目的:
- 开发和验证机器学习 (ML) 模型,用于预测GAD的治疗反应.
- 确定从MEMI与SM中GAD严重程度,认知功能和正念的差异可靠变化的基线预测因素.
- 为精准医学策略提供信息,以优化GAD治疗选择.
主要方法:
- 利用了一项随机对照试验的数据来比较MEMI和SM的GAD (N=110).
- 训练并评估了三种ML模型,以预测一个月后的后续结果.
- 测试了11个基线变量,包括人口统计,认知指标和临床症状作为预测因素.
主要成果:
- 最终的ML模型显示出强大的预测性能 (AUC = .752.886).
- 更高的GAD严重程度预测了更大的症状改善,但执行功能收益较少.
- 提高的坚持性认知,抑制性失控和口头不流性预测了多个领域的更好的结果.
- 更大的设置转移能力和特征正念预测了GAD症状和正念的更强烈改善.
- 年龄较大,女性性别和白人种族与差异化治疗益处有关.
结论:
- 机器学习模型可以有效地预测GAD的差异性治疗反应.
- 包括临床,认知和社会人口统计因素在内的规范性预测变量可以指导干预选择.
- 这些发现支持使用精准医学来优化GAD的数字心理健康干预措施.
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