预测治疗后症状严重程度的成年人接受心理治疗在日常护理一般性焦虑障碍:一个机器学习方法
H Delamain1, J E J Buckman2, C O'Driscoll1
1CORE Data Lab, Centre for Outcomes Research and Effectiveness (CORE), Research Department of Clinical, Educational and Health Psychology, UCL, London, United Kingdom.
Psychiatry research
|April 12, 2024
概括
一个新的模型使用治疗前的数据准确地预测了泛性焦虑障碍 (GAD) 治疗结果. 这种工具有助于临床医生和患者对治疗有效性做出明智的决定,改善GAD管理.
科学领域:
- 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
- 临床心理学 临床心理学
背景情况:
- 一般性焦虑障碍 (GAD) 的复发率很高,大约一半的患者在初始治疗后无法恢复.
- 目前,没有经过验证的模型可以预测GAD患者的治疗益处.
- 这种差距阻碍了个性化治疗规划和个人和临床医生的知情决策.
研究的目的:
- 开发和验证治疗后GAD症状严重程度的准确和可解释的预测模型.
- 确定GAD患者治疗反应的关键预测因素.
- 提供一个工具,可以为临床决策和患者的期望提供信息.
主要方法:
- 利用了来自15859名接受GAD治疗的成年人在八个改善获得心理治疗 (IAPT) 服务中的数据.
- 通过10倍交叉验证与更简单的临床模型比较了13个机器学习算法.
- 最好的模型在坚持数据集上得到验证,可解释性测量确定了关键预测因素.
主要成果:
- 一个贝叶斯增量回归树 (BART) 模型显著超过了比较模型 (MSE=16.54,R2=0.33).
- 确定的主要预测因素包括PHQ-9无情,GAD-7项目 (烦/易怒,不安,恐惧) 和转诊评估等待时间.
- 该模型仅使用治疗前的数据准确预测了治疗后的GAD严重程度.
结论:
- 开发的BART模型提供了一个准确和可解释的方法来预测GAD治疗结果.
- 这种预测能力可以帮助临床医生和患者了解潜在的治疗益处.
- 该模型的性能在临床上有意义的误差范围内,这表明在GAD管理中具有实际实用性.
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