2023
DOI: 10.3389/fninf.2023.1273827
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Prognostic estimation for acute ischemic stroke patients undergoing mechanical thrombectomy within an extended therapeutic window using an interpretable machine learning model

Lin Tong,
Yun Sun,
Yueqi Zhu
et al.

Abstract: BackgroundMechanical thrombectomy (MT) is effective for acute ischemic stroke with large vessel occlusion (AIS-LVO) within an extended therapeutic window. However, successful reperfusion does not guarantee positive prognosis, with around 40–50% of cases yielding favorable outcomes. Preoperative prediction of patient outcomes is essential to identify those who may benefit from MT. Although machine learning (ML) has shown promise in handling variables with non-linear relationships in prediction models, its “blac… Show more

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