Utilizing imbalanced electronic health records to predict acute kidney injury by ensemble learning and time series model

BACKGROUND: Acute Kidney Injury (AKI) is a shared complication among Intensive Care Unit (ICU), marked by high cost, high morbidity and high mortality. As the early prediction of AKI is critical for patients’ outcomes and data mining is such a powerful prediction tool, many AKI prediction models bas...

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Detalles Bibliográficos
Autores principales: Wang, Yuan, Wei, Yake, Yang, Hao, Li, Jingwei, Zhou, Yubo, Wu, Qin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2020
Materias:
Research Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7507620/
https://www.ncbi.nlm.nih.gov/pubmed/32957977
http://dx.doi.org/10.1186/s12911-020-01245-4

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7507620/
https://www.ncbi.nlm.nih.gov/pubmed/32957977
http://dx.doi.org/10.1186/s12911-020-01245-4

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