Causal risk factor discovery for severe acute kidney injury using electronic health records.

Journal: BMC medical informatics and decision making
Published Date: Mar 22, 2018

Abstract

BACKGROUND: Acute kidney injury (AKI), characterized by abrupt deterioration of renal function, is a common clinical event among hospitalized patients and it is associated with high morbidity and mortality. AKI is defined in three stages with stage-3 being the most severe phase which is irreversible. It is important to effectively discover the true risk factors in order to identify high-risk AKI patients and allow better targeting of tailored interventions. However, Stage-3 AKI patients are very rare (only 0.2% of AKI patients) with a large scale of features available in EHR (1917 potential risk features), yielding a scenario unfeasible for any correlation-based feature selection or modeling method. This study aims to discover the key factors and improve the detection of Stage-3 AKI.

Authors

  • Weiqi Chen
    School of Computer Science, University of Birmingham, Edgbaston, Birmingham, B15 2TT, UK.
  • Yong Hu
    Big Data Decision Institute, Jinan University, Guangzhou, China.
  • Xiangzhou Zhang
    Big Data Decision Institute, Jinan University, Guangzhou, China.
  • Lijuan Wu
    Big Data Decision Institute, Jinan University, Guangzhou, China.
  • Kang Liu
    Technology and Engineering Center for Space Utilization, Chinese Academy of Sciences, Beijing, 100094, China. liukang@csu.ac.cn.
  • Jianqin He
    School of Information Science and Technology, Guangdong University of Foreign Studies, Guangzhou, China.
  • Zilin Tang
    Big Data Decision Institute (BDDI), Jinan University, Tianhe, Guangzhou, 510632, China.
  • Xing Song
    Department of Internal Medicine, Division of Medical Informatics, University of Kansas Medical Center, Kansas City, Kansas, USA.
  • Lemuel R Waitman
    Department of Internal Medicine, Division of Medical Informatics, University of Kansas Medical Center, Kansas City, Missouri, USA.
  • Mei Liu
    Department of Internal Medicine, Division of Medical Informatics, University of Kansas Medical Center, Kansas City, Missouri, USA.

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