Prediction of cervical spine injury in young pediatric patients: an optimal trees artificial intelligence approach.

Journal: Journal of pediatric surgery
PMID: 30928154

Abstract

BACKGROUND: Cervical spine injuries (CSI) are a major concern in young pediatric trauma patients. The consequences of missed injuries and difficulties in injury clearance for non-verbal patients have led to a tendency to image young children. Imaging, particularly computed tomography (CT) scans, presents risks including radiation-induced carcinogenesis. In this study we leverage machine learning methods to develop highly accurate clinical decision rules to predict pediatric CSI.

Authors

  • Dimitris Bertsimas
    Dimitris Bertsimas, Jack Dunn, Colin Pawlowski, John Silberholz, Alexander Weinstein, and Ying Daisy Zhuo, Massachusetts Institute of Technology, Cambridge; Eddy Chen, Massachusetts General Hospital Cancer Center; Harvard Medical School; Aymen A. Elfiky, Dana-Farber Cancer Institute; Brigham and Women's Hospital; Harvard Medical School, Boston, MA.
  • Peter T Masiakos
    Division of Pediatric Surgery, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA. Electronic address: pmasiakos@partners.org.
  • Konstantinos S Mylonas
    Society of Junior Doctors, Athens, Greece; Aristotle University of Thessaloniki, Medical School, Thessaloniki, Greece.
  • Holly Wiberg
    Operations Research Center, Massachusetts Institute of Technology, Cambridge, MA, USA.

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