Seeing the forest beyond the trees: Predicting survival in burn patients with machine learning.

Journal: American journal of surgery
Published Date: Nov 7, 2017

Abstract

BACKGROUND: This study aims to identify predictors of survival for burn patients at the patient and hospital level using machine learning techniques.

Authors

  • Adrienne N Cobb
    Loyola University Medical Center, Department of Surgery, 2160 S. 1st Avenue, Maywood, IL 60153, USA; One:MAP Section of Surgical Analytics, Department of Surgery, Loyola University Chicago, 2160 S. 1st Avenue, Maywood, IL 60153, USA. Electronic address: adcobb@lumc.edu.
  • Witawat Daungjaiboon
    DePaul University, College of Computing and Digital Media, Department of Predictive Analytics, 243 South Wabash Avenue, Chicago, IL 60604, USA. Electronic address: dwitawat@gmail.com.
  • Sarah A Brownlee
    One:MAP Section of Surgical Analytics, Department of Surgery, Loyola University Chicago, 2160 S. 1st Avenue, Maywood, IL 60153, USA. Electronic address: sbrownlee@luc.edu.
  • Anthony J Baldea
    Loyola University Medical Center, Department of Surgery, 2160 S. 1st Avenue, Maywood, IL 60153, USA. Electronic address: abaldea@lumc.edu.
  • Arthur P Sanford
    Loyola University Medical Center, Department of Surgery, 2160 S. 1st Avenue, Maywood, IL 60153, USA. Electronic address: asanford@lumc.edu.
  • Michael M Mosier
    Loyola University Medical Center, Department of Surgery, 2160 S. 1st Avenue, Maywood, IL 60153, USA. Electronic address: mmosier@lumc.edu.
  • Paul C Kuo
    Loyola University Medical Center, Department of Surgery, 2160 S. 1st Avenue, Maywood, IL 60153, USA; One:MAP Section of Surgical Analytics, Department of Surgery, Loyola University Chicago, 2160 S. 1st Avenue, Maywood, IL 60153, USA. Electronic address: paul.kuo@luhs.org.

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