Mining patient-specific and contextual data with machine learning technologies to predict cancellation of children's surgery.

Journal: International journal of medical informatics
Published Date: Jun 8, 2019

Abstract

BACKGROUND: Last-minute surgery cancellation represents a major wastage of resources and can cause significant inconvenience to patients. Our objectives in this study were: 1) To develop predictive models of last-minute surgery cancellation, utilizing machine learning technologies, from patient-specific and contextual data from two distinct pediatric surgical sites of a single institution; and 2) to identify specific key predictors that impact children's risk of day-of-surgery cancellation.

Authors

  • Lei Liu
    Department of Science and Technology, Beijing Shijitan Hospital, Capital Medical University, Beijing, China.
  • Yizhao Ni
    Division of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, Cincinnati, OH.
  • Nanhua Zhang
    College of Medicine, University of Cincinnati, Cincinnati, OH, USA; Division of Biostatistics and Epidemiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA.
  • J Nick Pratap
    College of Medicine, University of Cincinnati, Cincinnati, OH, USA; Department of Anesthesia, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA. Electronic address: jayant.pratap@cchmc.org.

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