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

ObjectiveAcute 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.MethodsIn a retrospective analysis, consecutive AIS and non-AIS inpatients...

Deskribapen osoa

Gorde:
Egile Nagusiak: Yao Jiang, Yingqiang Dang, Qian Wu, Boyao Yuan, Lina Gao, Chongge You
Formatua: Artigo
Hizkuntza:Inglês
Argitaratua: Frontiers Media S.A. 2024-03-01
Saila:Frontiers in Neurology
Gaiak:
acute ischemic stroke
novel phenotypes
machine learning
clustering algorithms
Clinlabomics models
Sarrera elektronikoa:https://www.frontiersin.org/articles/10.3389/fneur.2024.1366307/full
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