Identification of clinically meaningful, overlapping obstructive respiratory disease subtypes via data-driven approaches in a primary care population

This study provides a data-driven dissection of the heterogeneity underlying obstructive airway diseases in a large, real-world population. Unsupervised machine learning applied to national-scale EHR data revealed distinct and partially stable subtypes that transcend conventional diagnostic boundaries. These findings highlight the complexity and overlap of airway disease phenotypes and demonstrate the value of clustering approaches for uncovering clinically and biologically meaningful subgroups. This work lays the foundation for further exploration into mechanisms and prognosis within and across airway disease phenotypes.