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Using machine learning to predict atrial fibrillation diagnosed after ischemic stroke
Selecting best candidates for prolonged poststroke cardiac monitoring in acute ischemic stroke (AIS) patients is still challenging. We aimed to develop a machine learning (ML) model to select AIS patients at high risk of poststroke atrial fibrillation (AF) for prolonged cardiac monitoring and then to compare ML model with traditional risk scores and classic statistical logistic regression (classic-LR) model.
Source: International Journal of Cardiology - November 11, 2021 Category: Cardiology Authors: Xiaohan Zheng, Fusang Wang, Juan Zhang, Xiaoli Cui, Fuping Jiang, Nihong Chen, Junshan Zhou, Jinsong Chen, Song Lin, Jianjun Zou Source Type: research