A deep learning backcasting approach to the electrolyte, metabolite, and acid-base parameters that predict risk in ICU patients.
Journal:
PloS one
Published Date:
Jan 1, 2020
Abstract
BACKGROUND: A powerful risk model allows clinicians, at the bedside, to ensure the early identification of and decision-making for patients showing signs of developing physiological instability during treatment. The aim of this study was to enhance the identification of patients at risk for deterioration through an accurate model using electrolyte, metabolite, and acid-base parameters near the end of patients' intensive care unit (ICU) stays.