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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Autori principali:	Xunliang Li, Peng Wang, Yuke Zhu, Wenman Zhao, Haifeng Pan, Deguang Wang
Natura:	Artigo
Lingua:	Inglês
Pubblicazione:	BMC 2024-05-01
Serie:	BMC Medical Informatics and Decision Making
Soggetti:	Acute kidney injury Machine learning Interpretability Prediction model Intensive care unit
Accesso online:	https://doi.org/10.1186/s12911-024-02537-9
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