Much attention is currently devoted to developing diagnostic classifiers for mental disorders. Complementing these efforts, we highlight the potential of machine learning to gain biological insights into the psychopathology and nosology of mental disorders. Studies to this end have mainly used brain imaging data, which can be obtained noninvasively from large cohorts and have repeatedly been argued to reveal potentially intermediate phenotypes. This may become particularly relevant in light of recent efforts to identify magnetic resonance imaging–derived biomarkers that yield insight into pathophysiological processes as well as to refine the taxonomy of mental illness. In particular, the accuracy of machine learning models may be used as dependent variables to identify features relevant to pathophysiology. Moreover, such approaches may help disentangle the dimensional (within diagnosis) and often overlapping (across diagnoses) symptomatology of psychiatric illness. We also point out a multiview perspective that combines data from different sources, bridging molecular and system-level information. Finally, we summarize recent efforts toward a data-driven definition of subtypes or disease entities through unsupervised and semisupervised approaches. The latter, blending unsupervised and supervised concepts, may represent a particularly promising avenue toward dissecting heterogeneous categories. Finally, we raise several technical and conceptual aspects related to the reviewed approaches. In particular, we discuss common pitfalls pertaining to flawed input data or analytic procedures that would likely lead to unreliable outputs.

目前，很多注意力都集中在开发精神障碍的诊断分类器上。作为对这些努力的补充，我们强调了机器学习在获得对精神障碍的精神病理学和疾病分类学的生物学见解方面的潜力。为此目的的研究主要使用脑成像数据，这些数据可以从大型队列中无创地获得，并且一再被认为可以揭示潜在的中间表型。鉴于最近确定磁共振成像衍生生物标志物的努力，这可能变得特别相关，这些生物标志物可以深入了解病理生理过程以及完善精神疾病的分类学。特别是，机器学习模型的准确性可以用作因变量来识别与病理生理学相关的特征. 此外，这些方法可能有助于理清精神疾病的维度（诊断内）和经常重叠（跨诊断）的症状。我们还指出了一个多视图视角，它结合了来自不同来源的数据，桥接了分子和系统级信息。最后，我们总结了最近通过无监督和半监督方法对亚型或疾病实体进行数据驱动定义的努力。后者混合了无监督和有监督的概念，可能代表了一种特别有前途的剖析异构类别的途径。最后，我们提出了与审查方法相关的几个技术和概念方面。特别是，我们讨论了与有缺陷的输入数据或可能导致不可靠输出的分析程序有关的常见陷阱。