Application of Machine Learning Techniques to High-Dimensional Clinical Data to Forecast Postoperative Complications.

Journal: PloS one
Published Date: May 27, 2016

Abstract

OBJECTIVE: To compare performance of risk prediction models for forecasting postoperative sepsis and acute kidney injury.

Authors

  • Paul Thottakkara
    Department of Anesthesiology, College of Medicine, University of Florida, Gainesville, Florida, United States of America.
  • Tezcan Ozrazgat-Baslanti
    Department of Medicine, University of Florida, Gainesville, FL USA.
  • Bradley B Hupf
    Department of Anesthesiology, College of Medicine, University of Florida, Gainesville, Florida, United States of America.
  • Parisa Rashidi
    Department of Biomedical Engineering, University of Florida, Gainesville, FL USA.
  • Panos Pardalos
    Industrial and Systems Engineering, University of Florida, Gainesville, Florida, United States of America.
  • Petar Momcilovic
    Industrial and Systems Engineering, University of Florida, Gainesville, Florida, United States of America.
  • Azra Bihorac
    Department of Medicine, University of Florida, Gainesville, FL USA.

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