Pontén Moa, Flygare Oskar, Bellander Martin, Karemyr Moa, Nilbrink Jannike, Hellner Clara, Ojala Olivia, Bjureberg Johan
Centre for Psychiatry Research, Department of Clinical Neuroscience, Stockholm, Karolinska Institutet, Sweden & Stockholm Health Care Services, Region Stockholm, Norra Stationsgatan 69, 113 64, Stockholm, Sweden.
BMC Psychiatry. 2024 Dec 18;24(1):904. doi: 10.1186/s12888-024-06391-x.
Nonsuicidal self-injury is a common health problem in adolescents and associated with future suicidal behavior. Predicting who will benefit from treatment is an urgent and a critical first step towards personalized treatment approaches. Machine-learning algorithms have been proposed as techniques that might outperform clinicians' judgment. The aim of this study was to explore clinician predictions of which adolescents would abstain from nonsuicidal self-injury after treatment as well as how these predictions match machine-learning algorithm predictions.
Data from a recent trial evaluating an internet-delivered emotion regulation therapy for adolescents with nonsuicidal self-injury was used. Clinician predictions of which patients would abstain from nonsuicidal self-injury (measured using the youth version of Deliberate Self-harm Inventory) were compared to a random forest model trained on the same available data from baseline assessments.
Both clinician (accuracy = 0.63) and model-based (accuracy = 0.67) predictions achieved significantly better accuracy than a model that classified all patients as reaching NSSI remission (accuracy = 0.49 [95% CI 0.41 to 0.58]), however there was no statistically significant difference between them. Adding clinician predictions to the random forest model did not improve accuracy. Emotion dysregulation was identified as the most important predictor of nonsuicidal self-injury absence.
Preliminary findings indicate comparable prediction accuracy between clinicians and a machine-learning algorithm in the psychological treatment of nonsuicidal self-injury in youth. As both prediction approaches achieved modest accuracy, the current results indicate the need for further research to enhance the predictive power of machine-learning algorithms. Machine learning model indicated that emotion dysregulation may be of importance in treatment planning, information that was not available from clinician predictions.
NCT03353961|| https://www.
gov/ , registered 2017-11-21. Preregistration at Open Science Framework: https://osf.io/vym96/ .
非自杀性自伤是青少年中常见的健康问题，且与未来的自杀行为相关。预测谁将从治疗中获益是迈向个性化治疗方法的紧迫且关键的第一步。机器学习算法已被提议作为可能优于临床医生判断的技术。本研究的目的是探讨临床医生对哪些青少年在治疗后会戒除非自杀性自伤的预测，以及这些预测与机器学习算法预测的匹配程度。
使用了近期一项评估针对非自杀性自伤青少年的互联网情绪调节疗法试验的数据。将临床医生对哪些患者会戒除非自杀性自伤（使用青少年版故意自伤量表进行测量）的预测与基于相同基线评估可用数据训练的随机森林模型进行比较。
临床医生（准确率 = 0.63）和基于模型（准确率 = 0.67）的预测均显著优于将所有患者分类为达到非自杀性自伤缓解的模型（准确率 = 0.49 [95% CI 0.41至0.58]），然而两者之间无统计学显著差异。将临床医生的预测添加到随机森林模型中并未提高准确率。情绪失调被确定为非自杀性自伤戒除的最重要预测因素。
初步研究结果表明，在青少年非自杀性自伤的心理治疗中，临床医生和机器学习算法的预测准确率相当。由于两种预测方法的准确率都一般，当前结果表明需要进一步研究以提高机器学习算法的预测能力。机器学习模型表明情绪失调在治疗规划中可能很重要，而这一信息无法从临床医生的预测中获得。
NCT03353961|| https://www.
gov/ ，于2017年11月21日注册。在开放科学框架下的预注册：https://osf.io/vym96/ 。
