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Personalized prediction of mortality in patients with acute ischemic stroke using explainable artificial intelligence
9
Zitationen
12
Autoren
2024
Jahr
Abstract
Complete renal function trajectories, including AKI and AKD, are vital for fitting mortality in AIS patients. An interpretable ML model effectively clarified its decision-making process for identifying AIS patients at risk of mortality. The AI-driven web application has the potential to contribute to the development of personalized early mortality prevention.
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