Using machine learning tools to predict outcomes for emergency department intensive care unit patients.

ABSTRACT: The number of critically ill patients has increased globally along with the rise in emergency visits. Mortality prediction for critical patients is vital for emergency care, which affects the distribution of emergency resources. Traditional scoring systems are designed for all emergency patients using a classic mathematical method, but risk factors in critically ill patients have complex interactions, so traditional scoring cannot as readily apply to them. As an accurate model for predicting the mortality of emergency department critically ill patients is lacking, this study's objective was to develop a scoring system using machine learning optimized for the unique case of critical patients in emergency departments. We conducted a retrospective cohort study in a tertiary medical center in

SUBMITTER: Zhai Q 

PROVIDER: S-EPMC7708467 | biostudies-literature | 2020 Dec

REPOSITORIES: biostudies-literature