Machine learning without borders? An adaptable tool to optimize mortality prediction in diverse clinical settings.

Journal: The journal of trauma and acute care surgery
PMID: 30059457

Abstract

BACKGROUND: Mortality prediction aids clinical decision making and is necessary for quality improvement initiatives. Validated metrics rely on prespecified variables and often require advanced diagnostics, which are unfeasible in resource-constrained contexts. We hypothesize that machine learning will generate superior mortality prediction in both high-income and low- and middle-income country cohorts.

Authors

  • S Ariane Christie
    From the Department of Surgery (S.A.C., R.A.C., C.J.), University of California San Francisco, San Francisco, California; Department of Biostatistics (A.E.H.), University of California Berkeley, Berkeley, California; Division of General Surgery (M.H.), Vancouver General Hospital, University of British Columbia, Vancouver, British Columbia, Canada; Littoral Regional Delegation of the Ministry of Public Health, Cameroon (F.N.D.-D.), Douala, Cameroon; Laquintinie Hospital of Douala, Douala, Cameroon (D.M., A.S.); Regional Hospital of Limbe, Limbe, Cameroon (A.C.M.); Catholic Hospital of Pouma, Pouma, Cameroon (P.N.); Department of Surgery (R.A.D.), University of California Los Angeles, Los Angeles, California; and Denver Health Medical Center and the University of Colorado, Denver, Colorado (M.J.C.).
  • Alan E Hubbard
  • Rachael A Callcut
    Division of General Surgery, Department of Surgery, School of Medicine, University of California San Francisco, San Francisco, California, United States of America.
  • Morad Hameed
  • Fanny Nadia Dissak-Delon
  • David Mekolo
  • Arabo Saidou
  • Alain Chichom Mefire
  • Pierre Nsongoo
  • Rochelle A Dicker
  • Mitchell Jay Cohen
  • Catherine Juillard

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