Psychological and Brain Sciences, Indiana University Bloomington, Bloomington, IN, USA.
J Child Psychol Psychiatry. 2024 Feb;65(2):248-250. doi: 10.1111/jcpp.13914. Epub 2023 Nov 3.
Clinical psychology and psychiatry have many 'holy grails' or research findings that are widely sought after but remain elusive. The use of machine learning (ML) models for treatment selection is one of these holy grails. Ahuvia et al. (Journal of Child Psychology and Psychiatry, 2023) recently analyzed a large trial (n = 996) of two distinct single-session interventions (SSIs) for internalizing distress and found little evidence that an ML model could predict differential treatment response. I discuss potential avenues for advancing SSI research. One avenue is the dissemination and implementation of SSIs, including how they interact with other treatments in routine care. Quantifying and critically questioning the promises of holy grails like ML models is sorely needed. Using simulation modeling to evaluate the relative merits of using ML models for treatment selection or using SSIs versus other treatment strategies may be another path forward.
临床心理学和精神病学有许多“圣杯”或广泛追求但仍难以捉摸的研究发现。机器学习 (ML) 模型在治疗选择中的应用就是其中之一。Ahuvia 等人（《儿童心理学和精神病学杂志》，2023 年）最近分析了一项针对两种不同单次干预 (SSI) 的大型试验（n=996），用于治疗内在困扰，发现几乎没有证据表明 ML 模型可以预测治疗反应的差异。我讨论了推进 SSI 研究的潜在途径。一个途径是推广和实施 SSI，包括它们与常规护理中其他治疗方法的相互作用。量化并批判性地质疑像 ML 模型这样的圣杯的承诺是非常必要的。使用模拟建模来评估使用 ML 模型进行治疗选择或使用 SSI 与其他治疗策略的相对优点可能是另一个前进的方向。
