Prediction of Long-Term Stroke Recurrence Using Machine Learning Models

Prediction of Long-Term Stroke Recurrence Using Machine Learning Models

Background: The long-term risk of recurrent ischemic stroke, estimated to be between 17% and 30%, cannot be reliably assessed at an individual level. Our goal was to study whether machine-learning can be trained to predict stroke recurrence and identify key clinical variables and assess whether perf...

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Bibliographic Details
Main Authors: Vida Abedi, Venkatesh Avula, Durgesh Chaudhary, Shima Shahjouei, Ayesha Khan, Christoph J Griessenauer, Jiang Li, Ramin Zand
Format: Article
Language:English
Published: MDPI AG 2021-03-01
Series:Journal of Clinical Medicine
Subjects:
healthcare
artificial intelligence
machine learning
interpretable machine learning
explainable machine learning
ischemic stroke
Online Access:https://www.mdpi.com/2077-0383/10/6/1286

Internet

https://www.mdpi.com/2077-0383/10/6/1286

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