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

Journal: BMC medical informatics and decision making
Published Date: Sep 21, 2020

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 models based on machine learning methods have been proposed. Our motivation is inspired by the fact that the incidence of AKI is a changing temporal sequence affected by the joint action of patients' daily drug combinations and their physiological indexes. However, most existing models have not considered such a temporal correlation. Besides, due to great challenges caused by sparse, high-dimensional and highly imbalanced clinical data, it is hard to achieve ideal performance.

Authors

  • Yuan Wang
    State Key Laboratory of Soil and Sustainable Agriculture, Changshu National Agro-Ecosystem Observation and Research Station, Institute of Soil Science, Chinese Academy of Sciences, Nanjing, China.
  • Yake Wei
    Center for Cyber Security, University of Electronic Science and Technology of China, ChengDu, China.
  • Hao Yang
    College of Agricultural Science and Engineering, Hohai University, Nanjing 210098, China.
  • Jingwei Li
    Department of Pulmonary and Critical Care Medicine, Med-X Center for Manufacturing, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, China.
  • Yubo Zhou
    Zhongshan Institute for Drug Discovery, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Zhongshan Tsuihang New District, Guangdong, 528400, PR China; School of Pharmacy, Zunyi Medical University, Zunyi, 563000, PR China; National Center for Drug Screening, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, 201203, PR China. Electronic address: ybzhou@simm.ac.cn.
  • Qin Wu
    Department of Critical Care Medicine, West China Hospital of Sichuan University, Chengdu, China.

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