A validation of machine learning-based risk scores in the prehospital setting.

Journal: PloS one
PMID: 31834920

Abstract

BACKGROUND: The triage of patients in prehospital care is a difficult task, and improved risk assessment tools are needed both at the dispatch center and on the ambulance to differentiate between low- and high-risk patients. This study validates a machine learning-based approach to generating risk scores based on hospital outcomes using routinely collected prehospital data.

Authors

  • Douglas Spangler
    Uppsala Center for Prehospital Research, Department of Surgical Sciences-Anesthesia and Intensive Care, Uppsala University, Uppsala, Sweden.
  • Thomas Hermansson
    Uppsala Ambulance Service, Uppsala University Hospital, Uppsala, Sweden.
  • David Smekal
    Uppsala Center for Prehospital Research, Department of Surgical Sciences-Anesthesia and Intensive Care, Uppsala University, Uppsala, Sweden.
  • Hans Blomberg
    Uppsala Center for Prehospital Research, Department of Surgical Sciences-Anesthesia and Intensive Care, Uppsala University, Uppsala, Sweden.

Keywords

External Resources

Popular Topics
Recent Journals