A Pilot Machine Learning Study Using Trauma Admission Data to Identify Risk for High Length of Stay.

Abstract

Authors

• David P Stonko
  Vanderbilt University School of Medicine, Nashville, TN, USA.
• Jennine H Weller
  Division of Trauma and Acute Care Surgery, The Johns Hopkins Hospital, The Johns Hopkins Department of Surgery, Baltimore, MD, USA.
• Andres J Gonzalez Salazar
  Division of Trauma and Acute Care Surgery, The Johns Hopkins Hospital, The Johns Hopkins Department of Surgery, Baltimore, MD, USA.
• Hossam Abdou
  R. Adams Cowley Shock Trauma Center, Baltimore, MD, USA.
• Joseph Edwards
  R. Adams Cowley Shock Trauma Center, Baltimore, MD, USA.
• Jeremiah Hinson
  Department of Emergency Medicine, Johns Hopkins University, Baltimore, MD 21218, United States.
• Scott Levin
  Department of Emergency Medicine, Johns Hopkins University, Baltimore, MD 21218, United States.
• James P Byrne
  Division of Trauma and Acute Care Surgery, The Johns Hopkins Hospital, The Johns Hopkins Department of Surgery, Baltimore, MD, USA.
• Joseph V Sakran
  Division of Trauma and Acute Care Surgery, The Johns Hopkins Hospital, The Johns Hopkins Department of Surgery, Baltimore, MD, USA.
• Caitlin W Hicks
  Division of Vascular Surgery and Endovascular Therapy, 1466The Johns Hopkins Medical Institutions, Baltimore, MD, USA.
• Elliott R Haut
  Division of Trauma and Acute Care Surgery, The Johns Hopkins Hospital, The Johns Hopkins Department of Surgery, Baltimore, MD, USA.
• Jonathan J Morrison
• Alistair J Kent
  Division of Trauma and Acute Care Surgery, The Johns Hopkins Hospital, The Johns Hopkins Department of Surgery, Baltimore, MD, USA.

Keywords

External Resources