Prediction of phenotypes by secretory biomarkers and machine learning in patients with chronic rhinosinusitis.
Journal:
European review for medical and pharmacological sciences
PMID:
39911041
Abstract
OBJECTIVE: Chronic rhinosinusitis (CRS) has traditionally been classified phenotypically according to the presence (CRSwNP) or absence (CRSsNP) of nasal polyps. However, the phenotypic dichotomy does not represent the complexity of the disease. Current research thus focuses on identifying underlying inflammatory mechanisms and distinguishing different endotypes. The objectives of this study were 1) to identify maximally predictive non-invasive biomarkers from nasal mucus, 2) to apply machine learning algorithms to use mucus-derived biomarkers to classify phenotype, and 3) to determine the feature importance of each mucus biomarker to phenotypes.