Interpretable machine learning model for predicting acute kidney injury in critically ill patients

Abstract Background This study aimed to create a method for promptly predicting acute kidney injury (AKI) in intensive care patients by applying interpretable, explainable artificial intelligence techniques. Methods Population data regarding intensive care patients were derived from the Medical Info...

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Hlavní autoři:	Xunliang Li, Peng Wang, Yuke Zhu, Wenman Zhao, Haifeng Pan, Deguang Wang
Médium:	Artigo
Jazyk:	Inglês
Vydáno:	BMC 2024-05-01
Edice:	BMC Medical Informatics and Decision Making
Témata:	Acute kidney injury Machine learning Interpretability Prediction model Intensive care unit
On-line přístup:	https://doi.org/10.1186/s12911-024-02537-9
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