Using a k-means clustering to identify novel phenotypes of acute ischemic stroke and development of its Clinlabomics models

Abstract excerpt

Objective: Acute ischemic stroke (AIS) is a heterogeneous condition. To stratify the heterogeneity, identify novel phenotypes, and develop Clinlabomics models of phenotypes that can conduct more personalized treatments for AIS. Methods: In a retrospective analysis, consecutive AIS and non-AIS inpatients were enrolled. An unsupervised k-means clustering algorithm was used to classify AIS patients into distinct...

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