Leveling Up: A Review of Machine Learning Models in the Cardiac ICU.

Journal: The American journal of medicine
Published Date:

Abstract

Machine learning has emerged as a significant tool to augment the medical decision-making process. Studies have steadily accrued detailing algorithms and models designed using machine learning to predict and anticipate pathologic states. The cardiac intensive care unit is an area where anticipation is crucial in the division between life and death. In this paper, we aim to review important studies describing the utility of machine learning algorithms to describe the future of artificial intelligence in the cardiac intensive care unit, especially in regards to the prediction of successful ventilatory weaning, acute respiratory distress syndrome, arrhythmia, and acute kidney injury.

Authors

  • Zain Khalpey
    Department of Surgery, University of Arizona College of Medicine, Tucson, Arizona, USA.
  • Parker Wilson
    Arizona College of Osteopathic Medicine, Glendale.
  • Yash Suri
    University of Arizona College of Medicine, Tucson.
  • Hunter Culbert
    University of Arizona College of Medicine, Tucson.
  • Jessa Deckwa
    Grand Canyon University, Phoenix, Ariz.
  • Amina Khalpey
    Globalhealth-AI LLC, Scottsdale, Ariz.
  • Brynne Rozell
    Arizona College of Osteopathic Medicine, Glendale.