A population-based study exploring phenotypic clusters and clinical outcomes in stroke using unsupervised machine learning approach.

A population-based study exploring phenotypic clusters and clinical outcomes in stroke using unsupervised machine learning approach.

Individuals developing stroke have varying clinical characteristics, demographic, and biochemical profiles. This heterogeneity in phenotypic characteristics can impact on cardiovascular disease (CVD) morbidity and mortality outcomes. This study uses a novel clustering approach to stratify individual...

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Bibliographic Details
Main Authors: Ralph K Akyea, George Ntaios, Evangelos Kontopantelis, Georgios Georgiopoulos, Daniele Soria, Folkert W Asselbergs, Joe Kai, Stephen F Weng, Nadeem Qureshi
Format: Article
Language:English
Published: Public Library of Science (PLoS) 2023-09-01
Series:PLOS Digital Health
Online Access:https://doi.org/10.1371/journal.pdig.0000334

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https://doi.org/10.1371/journal.pdig.0000334

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