Machine learning combining CT findings and clinical parameters improves prediction of length of stay and ICU admission in torso trauma.

Journal: European radiology
Published Date: Jan 21, 2021

Abstract

OBJECTIVE: To develop machine learning (ML) models capable of predicting ICU admission and extended length of stay (LOS) after torso (chest, abdomen, or pelvis) trauma, by using clinical and/or imaging data.

Authors

  • Pedro Vinícius Staziaki
    Department of Radiology, Boston Medical Center, Boston University School of Medicine, 820 Harrison Ave, FGH Building, 4th Floor, Boston, MA, 02114, USA. staziaki@gmail.com.
  • Di Wu
    University of Melbourne, Melbourne, VIC 3010 Australia.
  • Jesse C Rayan
    Division of Abdominal Imaging, Department of Radiology, Massachusetts General Hospital,, Boston, MA, USA.
  • Irene Dixe de Oliveira Santo
    Department of Radiology, Boston Medical Center, Boston University School of Medicine, 820 Harrison Ave, FGH Building, 4th Floor, Boston, MA, 02114, USA.
  • Feng Nan
    Department of Electrical and Computer Engineering, Boston University College of Engineering, Boston, MA, USA.
  • Aaron Maybury
    Department of Radiology, Boston Medical Center, Boston University School of Medicine, 820 Harrison Ave, FGH Building, 4th Floor, Boston, MA, 02114, USA.
  • Neha Gangasani
    Department of Radiology, Boston Medical Center, Boston University School of Medicine, 820 Harrison Ave, FGH Building, 4th Floor, Boston, MA, 02114, USA.
  • Ilan Benador
    Department of Radiology, Boston Medical Center, Boston University School of Medicine, 820 Harrison Ave, FGH Building, 4th Floor, Boston, MA, 02114, USA.
  • Venkatesh Saligrama
    Department of Electrical & Computer Engineering, and Division of Systems Engineering, Boston University, 8 Saint Mary's Street, Boston, MA 02215, United States.
  • Jonathan Scalera
  • Stephan W Anderson
    Department of Radiology, Boston Medical Center, Boston University School of Medicine, 820 Harrison Ave, FGH Building, 4th Floor, Boston, MA, 02114, USA.

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