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

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

Abstract 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 mo...

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Bibliographic Details
Main Authors: Yuan Wang, Yake Wei, Hao Yang, Jingwei Li, Yubo Zhou, Qin Wu
Format: Article
Language:English
Published: BMC 2020-09-01
Series:BMC Medical Informatics and Decision Making
Subjects:
Acute kidney injury (AKI)
Prediction
Ensemble learning
ETSM
Drug combination
Online Access:http://link.springer.com/article/10.1186/s12911-020-01245-4

Internet

http://link.springer.com/article/10.1186/s12911-020-01245-4

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