Walker Abigail, Surda Pavol
Department of ENT Surgery, St George's Hospital, London, UK.
Department of ENT Surgery, Guy's Hospital, London, UK.
Ann Otol Rhinol Laryngol. 2019 Dec;128(12):1170-1176. doi: 10.1177/0003489419863822. Epub 2019 Jul 18.
This article reviews the principles of unsupervised learning, a novel technique which has increasingly been reported as a tool for the investigation of chronic rhinosinusitis (CRS). It represents a paradigm shift from the traditional approach to investigating CRS based upon the clinically recognized phenotypes of "with polyps" and "without polyps" and instead relies upon the application of complex mathematical models to derive subgroups which can then be further examined. This review article reports on the principles which underlie this investigative technique and some of the published examples in CRS.
This review summarizes the different types of unsupervised learning techniques which have been described and briefly expounds upon their useful applications. A literature review of studies which have unsupervised learning is then presented to provide a practical guide to its uses and some of the new directions of investigations suggested by their findings.
The commonest unsupervised learning technique applied to rhinology research is cluster analysis, which can be further subdivided into hierarchical and non-hierarchical approaches. The mathematical principles which underpin these approaches are explained within this article. Studies which have used these techniques can be broadly divided into those which have used clinical data only and that which includes biomarkers. Studies which include biomarkers adhere closely to the established canon of CRS disease phenotypes, while those that use clinical data may diverge from the typical "polyp versus non-polyp" phenotypes and reflect subgroups of patients who share common symptom modifiers.
Artificial intelligence is increasingly influential in health care research and machine learning techniques have been reported in the investigation of CRS, promising several interesting new avenues for research. However, when critically appraising studies which use this technique, the reader needs to be with the limitations and appropriate uses of its application.
本文回顾了无监督学习的原理，这是一种越来越多地被报道用于慢性鼻-鼻窦炎（CRS）研究的新技术。它代表了一种从基于临床公认的“有息肉”和“无息肉”表型来研究CRS的传统方法的范式转变，而是依靠应用复杂的数学模型来推导亚组，然后对这些亚组进行进一步研究。这篇综述文章报道了这种研究技术背后的原理以及CRS领域一些已发表的实例。
本综述总结了已描述的不同类型的无监督学习技术，并简要阐述了它们的实际应用。然后对采用无监督学习的研究进行文献综述，以提供其应用的实用指南以及其研究结果所提示的一些新的研究方向。
应用于鼻科学研究的最常见的无监督学习技术是聚类分析，它可进一步细分为层次聚类和非层次聚类方法。本文解释了这些方法背后的数学原理。使用这些技术的研究大致可分为仅使用临床数据的研究和包含生物标志物的研究。包含生物标志物的研究紧密遵循CRS疾病表型的既定标准，而使用临床数据的研究可能与典型的“息肉与非息肉”表型不同，反映出具有共同症状修饰因素的患者亚组。
人工智能在医疗保健研究中的影响力日益增强，机器学习技术已被报道用于CRS的研究，有望开辟几个有趣的新研究途径。然而，在批判性地评估使用该技术的研究时，读者需要了解其应用的局限性和恰当用途。
