Predicting Clinical Outcomes in Acute Ischemic Stroke Patients Undergoing Endovascular Thrombectomy with Machine Learning
- 24 January 2021
- journal article
- review article
- Published by Springer Science and Business Media LLC in Clinical Neuroradiology
- Vol. 31 (4), 1121-1130
- https://doi.org/10.1007/s00062-020-00990-3
Abstract
Conventional predictive models are based on a combination of clinical and neuroimaging parameters using traditional statistical approaches. Emerging studies have shown that the machine learning (ML) prediction models with multiple pretreatment clinical variables have the potential to accurately prognosticate the outcomes in acute ischemic stroke (AIS) patients undergoing thrombectomy, and hence identify patients suitable for thrombectomy. This article summarizes the published studies on ML models in large vessel occlusion AIS patients undergoing thrombectomy.Keywords
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