Early prediction of hemodynamic interventions in the intensive care unit using machine learning.

Journal: Critical care (London, England)
Published Date: Nov 14, 2021

Abstract

BACKGROUND: Timely recognition of hemodynamic instability in critically ill patients enables increased vigilance and early treatment opportunities. We develop the Hemodynamic Stability Index (HSI), which highlights situational awareness of possible hemodynamic instability occurring at the bedside and to prompt assessment for potential hemodynamic interventions.

Authors

  • Asif Rahman
    Philips Research North America, Cambridge, MA, United States.
  • Yale Chang
    Philips Research North America, Cambridge, MA, 02141, USA.
  • Junzi Dong
    Hearing Research Center and Department of Biomedical Engineering, Boston University , Boston, Massachusetts 02215.
  • Bryan Conroy
    Philips Research North America, Cambridge, MA, USA.
  • Annamalai Natarajan
    Philips Research North America, Cambridge, MA, 02141, USA.
  • Takahiro Kinoshita
    Department of Gastric Surgery, National Cancer Center Hospital East, Kashiwa, Chiba, Japan.
  • Francesco Vicario
    Philips Research North America, Cambridge, MA, 02141, USA.
  • Joseph Frassica
    Philips Research North America, Cambridge, MA, 02141, USA.
  • Minnan Xu-Wilson
    Philips Research North America, Cambridge, MA, United States.

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