Improved Prediction of Older Adult Discharge After Trauma Using a Novel Machine Learning Paradigm.

Journal: The Journal of surgical research
Published Date: Oct 7, 2021

Abstract

BACKGROUND: The ability to reliably predict outcomes after trauma in older adults (age ≥ 65 y) is critical for clinical decision making. Using novel machine-learning techniques, we sought to design a nonlinear, competing risks paradigm for prediction of older adult discharge disposition following injury.

Authors

  • Rachel S Morris
    Department of Surgery, Medical College of Wisconsin, Milwaukee, Wisconsin. Electronic address: ramorris@mcw.edu.
  • Christopher J Tignanelli
    From the Department of Surgery (C.J.T., G.B., G.B.M.), University of Minnesota, Minneapolis, Minnesota; Institute for Health Informatics (C.J.T., G.M.S., R.F., R.M., B.C.K., S.P., E.A.L., G.B.M.), University of Minnesota, Minneapolis, Minnesota; Department of Surgery (C.J.T., J.L.G.), North Memorial Health Hospital, Robbinsdale, Minnesota; North Memorial Health Hospital Emergency Medical Services (A.L.T.), Robbinsdale, Minnesota; and Department of Emergency Medicine (J.W.L.), North Memorial Health Hospital Emergency Medical Services, Robbinsdale, Minnesota.
  • Terri deRoon-Cassini
    Department of Surgery, Medical College of Wisconsin, Milwaukee, Wisconsin.
  • Purushottam Laud
    Institute for Health and Equity, Medical College of Wisconsin, Milwaukee, Wisconsin.
  • Rodney Sparapani
    1 Institute for Health and Equity Division of Biostatistics Medical College of Wisconsin Milwaukee WI.

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