Unsupervised Learning Techniques for the Investigation of Chronic Rhinosinusitis. (December 2019)

• Record Type: Journal Article
• Authors: Walker, Abigail; Surda, Pavol
• Is Part Of: Annals of otology, rhinology & laryngology. Volume 128:Number 12(2019)
• Page Start: 1170
• Page End: 1176
• Publication Date: 2019-12
• Subjects: machine learning -- artificial intelligence -- chronic rhinosinusitis -- nasal polyposis -- epidemiology; Otolaryngology -- Periodicals; 617.51
• DOI: 10.1177/0003489419863822
• Languages: English
• ISSNs: 0003-4894

Abstract

Objectives: 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. Methods: 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. Results: 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. Summary: 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 au fait with the limitations and appropriate uses of its application.