Centre for Psychiatry Research, Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden.
Stockholm Healthcare Services, Stockholm County Council, Stockholm, Sweden.
Int J Methods Psychiatr Res. 2018 Mar;27(1). doi: 10.1002/mpr.1576. Epub 2017 Jul 28.
There are no consistent predictors of treatment outcome in paediatric obsessive-compulsive disorder (OCD). One reason for this might be the use of suboptimal statistical methodology. Machine learning is an approach to efficiently analyse complex data. Machine learning has been widely used within other fields, but has rarely been tested in the prediction of paediatric mental health treatment outcomes.
To test four different machine learning methods in the prediction of treatment response in a sample of paediatric OCD patients who had received Internet-delivered cognitive behaviour therapy (ICBT).
Participants were 61 adolescents (12-17 years) who enrolled in a randomized controlled trial and received ICBT. All clinical baseline variables were used to predict strictly defined treatment response status three months after ICBT. Four machine learning algorithms were implemented. For comparison, we also employed a traditional logistic regression approach.
Multivariate logistic regression could not detect any significant predictors. In contrast, all four machine learning algorithms performed well in the prediction of treatment response, with 75 to 83% accuracy.
The results suggest that machine learning algorithms can successfully be applied to predict paediatric OCD treatment outcome. Validation studies and studies in other disorders are warranted.
儿童强迫症（OCD）的治疗结果没有一致的预测因素。原因之一可能是使用了不优的统计方法。机器学习是一种有效分析复杂数据的方法。机器学习已在其他领域得到广泛应用，但在儿童心理健康治疗结果的预测中很少进行测试。
在接受互联网认知行为疗法（ICBT）的儿童 OCD 患者样本中，测试四种不同的机器学习方法对治疗反应的预测能力。
参与者为 61 名青少年（12-17 岁），他们参加了一项随机对照试验并接受了 ICBT。所有临床基线变量均用于预测 ICBT 三个月后的严格定义的治疗反应状态。实施了四种机器学习算法。为了比较，我们还采用了传统的逻辑回归方法。
多变量逻辑回归无法检测到任何显著的预测因子。相比之下，所有四种机器学习算法在治疗反应的预测中表现良好，准确率为 75%至 83%。
结果表明，机器学习算法可成功应用于预测儿童 OCD 治疗结果。需要进行验证研究和其他障碍的研究。
