Dynamic multi-outcome prediction after injury: Applying adaptive machine learning for precision medicine in trauma.

Journal: PloS one
PMID: 30970030

Abstract

OBJECTIVE: Machine learning techniques have demonstrated superior discrimination compared to conventional statistical approaches in predicting trauma death. The objective of this study is to evaluate whether machine learning algorithms can be used to assess risk and dynamically identify patient-specific modifiable factors critical to patient trajectory for multiple key outcomes after severe injury.

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.).
  • Amanda S Conroy
    Department of Surgery, Zuckerberg San Francisco General Hospital and Trauma Center and the University of California, San Francisco; San Francisco, California, United States of America.
  • 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.
  • Alan E Hubbard
  • Mitchell J Cohen
    Division of General Surgery, Department of Surgery, School of Medicine, University of California San Francisco, San Francisco, California, United States of America.

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