Improving the Accuracy of Scores to Predict Gastrostomy after Intracerebral Hemorrhage with Machine Learning.

Journal: Journal of stroke and cerebrovascular diseases : the official journal of National Stroke Association
Published Date: Dec 1, 2018

Abstract

BACKGROUND: Gastrostomy placement after intracerebral hemorrhage indicates the need for continued medical care and predicts patient dependence. Our objective was to determine the optimal machine learning technique to predict gastrostomy.

Authors

  • Ravi Garg
    Center for Healthcare Studies, Northwestern University, Chicago, Illinois.
  • Shyam Prabhakaran
    Center for Healthcare Studies, Northwestern University, Chicago, Illinois.
  • Jane L Holl
    Center for Healthcare Studies, Northwestern University, Chicago, Illinois.
  • Yuan Luo
    Department of Preventive Medicine, Northwestern University, Feinberg School of Medicine, Chicago, IL 60611, USA.
  • Roland Faigle
    Department of Neurology, Johns Hopkins University, Baltimore, Maryland.
  • Konrad Kording
    Laura Prosser, PhD, PTR is a Assistant Professor of Pediatrics, the Perelman School of Medicine, University of Pennsylvania and a physical therapist, Children's Hospital of Philadelphia.
  • Andrew M Naidech
    From the Department of Preventive Medicine (H.W., T.S., M.R.H, J.X.M, J.S.N, Y.L.), Department of Neurological Surgery (E.J.H.), and Department of Neurology (A.M.N.), Northwestern University Feinberg School of Medicine, Chicago, Illinois, USA; IRCCS Istituto delle Scienze Neurologiche di Bologna, Department of Neurology and Stroke Center (A.Z., L.B.), Maggiore Hospital, Bologna, Italy; Department of Biomedical and Neuromotor Sciences (DIBINEM) (S.G.), Alma Mater Studiorum-University of Bologna, Bologna, Italy.

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