使用机器学习来预测在强化治疗PTSD时的突然增长
Nicole M Christ1, Ryan A Schubert1, Rhea Mundle1
1Department of Psychiatry and Behavioral Sciences, Rush University Medical Center, Chicago, IL, USA.
Journal of anxiety disorders
|October 23, 2023
概括
在强化治疗创伤后应激障碍 (PTSD) 中,突然获得的收益发生在19-31%的患者中. 虽然最初预测了更好的结果,但当考虑整体症状减少时,这些收益并没有显著改善长期结果.
科学领域:
- 精神病学是一个精神病学.
- 临床心理学 临床心理学
- 行为科学 行为科学
背景情况:
- 创伤后应激障碍 (PTSD) 治疗的突然收益预测了更好的结果,但预测因素仍然不清楚.
- 在密集的PTSD治疗计划 (ITPs) 中,没有研究过突然的增长.
研究的目的:
- 在2周和3周的ITPs中探索突然的收益.
- 评估突发增长对PTSD治疗后和随访期间严重程度的影响.
- 通过机器学习来评估突然收益的可预测性.
主要方法:
- 在3周ITP的465名参与者和2周ITP的235名参与者中检查了突然的增长.
- 在治疗后和3个月的随访期间评估PTSD症状严重程度.
- 利用三种机器学习算法来预测突然的收益.
主要成果:
- 在31% (3周ITP) 和19% (2周ITP) 的参与者中观察到突然增长.
- 突然的增长预测在治疗后和随访时,PTSD症状减少更大.
- 在控制整体症状减少后,突然增强对随访结果的预测效应并不显著.
- 机器学习模型在预测突然增益方面表现不佳 (AUC < 0.7).
结论:
- 在密集的PTSD治疗中会出现突然的增长,但可能具有有限的长期影响.
- 即使使用先进的机器学习技术,预测突然的收益仍然具有挑战性.
- 需要进一步的研究来理解和潜在地预测PTSD治疗中的突然收益.
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