Psychiatric diseases are very heterogeneous both in clinical manifestation and etiology. With the recent rise of using machine learning techniques to attempt to diagnose and prognose these disorders, the issue of heterogeneity becomes increasingly important. With the growing interest in personalized medicine, it becomes even more important to not only classify someone as a patient with a certain disorder, its treatment needs a more precise definition of the underlying neurobiology, since different biological origins of the same disease may require (very) different treatments. We review the possible contributions that machine learning techniques could make to explore the heterogeneous nature of psychiatric disorders with a focus on schizophrenia. First we will review how heterogeneity shows up and how machine learning, or multivariate pattern recognition methods in general, can be used to discover it. Secondly, we will discuss the possible uses of these techniques to attack heterogeneity, leading to improved predictions and understanding of the neurobiological background of the disorder.

中文翻译：

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改进个体预测：用于检测和攻击精神分裂症（和其他精神疾病）异质性的机器学习方法

精神疾病在临床表现和病因学上都非常多样化。随着最近使用机器学习技术尝试诊断和预测这些疾病的兴起，异质性问题变得越来越重要。随着对个性化医疗的兴趣日益浓厚，不仅将某人归类为患有某种疾病的患者，其治疗还需要对潜在的神经生物学进行更精确的定义，因为同一疾病的不同生物学起源可能需要（非常) 不同的处理。我们回顾了机器学习技术在探索精神疾病的异质性方面可能做出的贡献，重点是精神分裂症。首先，我们将回顾异质性如何出现以及机器学习如何，或一般的多元模式识别方法，可以用来发现它。其次，我们将讨论这些技术在攻击异质性方面的可能用途，从而改进对疾病神经生物学背景的预测和理解。