Department of Psychiatry, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht Univeristy, Utrecht, The Netherlands.
Schizophr Res. 2019 Dec;214:34-42. doi: 10.1016/j.schres.2017.10.023. Epub 2017 Nov 1.
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.
精神疾病在临床表现和病因上都非常具有异质性。随着机器学习技术在诊断和预测这些疾病方面的应用日益增多，异质性问题变得越来越重要。随着个性化医疗的兴起，不仅要将某人归类为患有某种疾病的患者，而且还需要更精确地定义潜在的神经生物学，因为同一种疾病的不同生物学起源可能需要（非常）不同的治疗方法。我们回顾了机器学习技术可能做出的贡献，以探讨精神疾病的异质性，重点是精神分裂症。首先，我们将回顾异质性的表现方式，以及机器学习或一般的多元模式识别方法如何用于发现它。其次，我们将讨论这些技术的可能用途，以攻克异质性，从而提高对疾病神经生物学背景的预测和理解。
