Stroke risk prediction using machine learning: a prospective cohort study of 0.5 million Chinese adults.

Matthew Chun Robert J ClarkeBenjamin J CairnsDavid CliftonDerrick BennettYiping ChenYu GuoPei PeiJun LvCanqing YuLing YangLiming LiZhengming ChenTingting Zhunull null

Published in: Journal of the American Medical Informatics Association : JAMIA (2021)

Among several approaches, an ensemble model combining both GBT and Cox models achieved the best performance for identifying individuals at high risk of stroke in a contemporary study of Chinese adults. The results highlight the potential value of expanding the use of ML in clinical practice.

Keyphrases

atrial fibrillation
clinical practice
convolutional neural network
machine learning
climate change
brain injury
deep learning
neural network