Department of Behavioral Neuroscience, Oregon Health & Science University, Portland, OR 97239, USA; Department of Medical Informatics and Clinical Epidemiology Oregon Health & Science University, Portland, OR 97239, USA.
Department of Behavioral Neuroscience, Oregon Health & Science University, Portland, OR 97239, USA.
Trends Cogn Sci. 2019 Jul;23(7):584-601. doi: 10.1016/j.tics.2019.03.009. Epub 2019 May 29.
The imprecise nature of psychiatric nosology restricts progress towards characterizing and treating mental health disorders. One issue is the 'heterogeneity problem': different causal mechanisms may relate to the same disorder, and multiple outcomes of interest can occur within one individual. Our review tackles this heterogeneity problem, providing considerations, concepts, and approaches for investigators examining human cognition and mental health. We highlight the difficulty of pure dimensional approaches due to 'the curse of dimensionality'. Computationally, we consider supervised and unsupervised statistical approaches to identify putative subtypes within a population. However, we emphasize that subtype identification should be linked to a particular outcome or question. We conclude with novel hybrid approaches that can identify subtypes tied to outcomes, and may help advance precision diagnostic and treatment tools.
精神病学分类学的不精确性限制了对精神健康障碍的特征和治疗的进展。一个问题是“异质性问题”：不同的因果机制可能与同一障碍有关，一个人可能会出现多种感兴趣的结果。我们的综述解决了这个异质性问题，为研究人类认知和精神健康的研究人员提供了考虑、概念和方法。我们强调了由于“维度诅咒”，纯粹的维度方法的困难。在计算方面，我们考虑了监督和无监督的统计方法来识别人群中的潜在亚型。然而，我们强调，亚型的识别应该与特定的结果或问题联系起来。最后，我们提出了新颖的混合方法，可以识别与结果相关的亚型，并可能有助于推进精准诊断和治疗工具。
