Machine learning, statistical learning and the future of biological research in psychiatry.
Machine learning, statistical learning and the future of biological research in psychiatry.
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DOI：
10.1017/s0033291716001367
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发表时间：
2016-09
影响因子：
6.9
通讯作者：
McGuffin P
中科院分区：
文献类型：
--
作者：
Iniesta R;Stahl D;McGuffin P
精神病学研究已经进入了“大数据”时代。数据集现在通常涉及数千个异质变量，包括临床、神经影像学、基因组、蛋白质组学、转录组学和其他“组学”测量。对这些数据集的分析是具有挑战性的，特别是当测量的数量超过个体的数量时，并且由于一些高度相关的主题和变量的数据缺失，可能会进一步复杂化。基于统计学习的模型是经典统计方法的自然扩展，但提供了更有效的方法来分析非常大的数据集。此外，这些模型的预测能力有望在开发决策支持系统中发挥作用。也就是说，可以引入临床环境和指导的方法，例如，诊断分类或个性化治疗。在这篇综述中，我们旨在概述统计学习方法在临床研究中的潜在益处。我们首先介绍不同环境下的大数据概念。然后，我们描述了现代统计学习模型如何在大数据集的实践中使用，以提取相关信息。最后，我们从研究和临床实践的角度讨论了在精神病学研究中使用统计学习的优势。
Psychiatric research has entered the age of ‘Big Data’. Datasets now routinely involve thousands of heterogeneous variables, including clinical, neuroimaging, genomic, proteomic, transcriptomic and other ‘omic’ measures. The analysis of these datasets is challenging, especially when the number of measurements exceeds the number of individuals, and may be further complicated by missing data for some subjects and variables that are highly correlated. Statistical learning-based models are a natural extension of classical statistical approaches but provide more effective methods to analyse very large datasets. In addition, the predictive capability of such models promises to be useful in developing decision support systems. That is, methods that can be introduced to clinical settings and guide, for example, diagnosis classification or personalized treatment. In this review, we aim to outline the potential benefits of statistical learning methods in clinical research. We first introduce the concept of Big Data in different environments. We then describe how modern statistical learning models can be used in practice on Big Datasets to extract relevant information. Finally, we discuss the strengths of using statistical learning in psychiatric studies, from both research and practical clinical points of view.