Data-driven clustering approach to identify novel phenotypes using multiple biomarkers in acute ischaemic stroke: A retrospective, multicentre cohort study

Data-driven clustering approach to identify novel phenotypes using multiple biomarkers in acute ischaemic stroke: A retrospective, multicentre cohort study

Summary: Background: Acute ischaemic stroke (AIS) is a highly heterogeneous disorder and warrants further investigation to stratify patients with different outcomes and treatment responses. Using a large-scale stroke registry cohort, we applied data-driven approach to identify novel phenotypes base...

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Bibliographic Details
Main Authors: Lingling Ding, Ravikiran Mane, Zhenzhou Wu, Yong Jiang, Xia Meng, Jing Jing, Weike Ou, Xueyun Wang, Yu Liu, Jinxi Lin, Xingquan Zhao, Hao Li, Yongjun Wang, Zixiao Li
Format: Article
Language:English
Published: Elsevier 2022-11-01
Series:EClinicalMedicine
Subjects:
Acute ischemic stroke
Machine Learning
Biomarkers
Phenotypes
Clinical outcome
Online Access:http://www.sciencedirect.com/science/article/pii/S2589537022003698

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http://www.sciencedirect.com/science/article/pii/S2589537022003698

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