Transforming Clinical Data into Actionable Prognosis Models: Machine-Learning Framework and Field-Deployable App to Predict Outcome of Ebola Patients.

Journal: PLoS neglected tropical diseases
PMID: 26991501

Abstract

BACKGROUND: Assessment of the response to the 2014-15 Ebola outbreak indicates the need for innovations in data collection, sharing, and use to improve case detection and treatment. Here we introduce a Machine Learning pipeline for Ebola Virus Disease (EVD) prognosis prediction, which packages the best models into a mobile app to be available in clinical care settings. The pipeline was trained on a public EVD clinical dataset, from 106 patients in Sierra Leone.

Authors

  • Andres Colubri
    Center for Systems Biology, Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, Massachusetts, United States of America.
  • Tom Silver
    Broad Institute of MIT and Harvard, Cambridge, Massachusetts, United States of America.
  • Terrence Fradet
    Fathom Information Design, Boston, Massachusetts, United States of America.
  • Kalliroi Retzepi
    Department of Psychiatry, Massachusetts General Hospital, Boston, Massachusetts, United States of America.
  • Ben Fry
    Fathom Information Design, Boston, Massachusetts, United States of America.
  • Pardis Sabeti
    Center for Systems Biology, Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, Massachusetts, United States of America.

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