Creating an automated trigger for sepsis clinical decision support at emergency department triage using machine learning.

Journal: PloS one
Published Date: Apr 6, 2017

Abstract

OBJECTIVE: To demonstrate the incremental benefit of using free text data in addition to vital sign and demographic data to identify patients with suspected infection in the emergency department.

Authors

  • Steven Horng
    Department of Emergency Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA dsontag@cs.nyu.edu.
  • David A Sontag
    Department of Electrical Engineering and Computer Science, Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America.
  • Yoni Halpern
    Department of Computer Science, New York University, New York, NY, USA dsontag@cs.nyu.edu.
  • Yacine Jernite
    Department of Computer Science, Courant Institute of Mathematical Sciences, New York University, New York, New York, United States of America.
  • Nathan I Shapiro
    Department of Emergency Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts, United States of America.
  • Larry A Nathanson
    Department of Emergency Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts, United States of America.

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