Machine learning approaches for predicting disposition of asthma and COPD exacerbations in the ED.

Journal: The American journal of emergency medicine
Published Date: Jun 28, 2018

Abstract

OBJECTIVE: The prediction of emergency department (ED) disposition at triage remains challenging. Machine learning approaches may enhance prediction. We compared the performance of several machine learning approaches for predicting two clinical outcomes (critical care and hospitalization) among ED patients with asthma or COPD exacerbation.

Authors

  • Tadahiro Goto
    Department of Emergency Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States of America. Electronic address: tag695@mail.harvard.edu.
  • Carlos A Camargo
    Department of Emergency Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States of America.
  • Mohammad Kamal Faridi
    Department of Emergency Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States of America.
  • Brian J Yun
    Harvard Medical School, 25 Shattuck Street, Boston, MA 02115, United States; Department of Emergency Medicine, Massachusetts General Hospital, 55 Fruit St., Boston, MA 02114, United States; Center for Research in Emergency Department Operations (CREDO), Department of Emergency Medicine, Massachusetts General Hospital, 55 Fruit St., Boston, MA 02114, United States. Electronic address: byun@partners.org.
  • Kohei Hasegawa
    Department of Emergency Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States of America.

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