Multi-perspective predictive modeling for acute kidney injury in general hospital populations using electronic medical records.

Journal: JAMIA open
Published Date: Nov 15, 2018

Abstract

OBJECTIVES: Acute kidney injury (AKI) in hospitalized patients puts them at much higher risk for developing future health problems such as chronic kidney disease, stroke, and heart disease. Accurate AKI prediction would allow timely prevention and intervention. However, current AKI prediction researches pay less attention to model building strategies that meet complex clinical application scenario. This study aims to build and evaluate AKI prediction models from multiple perspectives that reflect different clinical applications.

Authors

  • Jianqin He
    School of Information Science and Technology, Guangdong University of Foreign Studies, Guangzhou, China.
  • 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.
  • 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.

Keywords

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