Fenn, Alexander, Connor Davis, Daniel M. Buckland, Neel Kapadia, Marshall Nichols, Michael Gao, William Knechtle, Suresh Balu, Mark Sendak, and B.Jason Theiling. “Development and Validation of Machine Learning Models to Predict Admission From Emergency Department to Inpatient and Intensive Care Units.” Annals of Emergency Medicine 78, no. 2 (August 2021): 290–302. https://doi.org/10.1016/j.annemergmed.2021.02.029
This study aimed to develop and validate 2 machine learning models that use historical and current-visit patient data from electronic health records to predict the probability of patient admission to either an inpatient unit or ICU at each hour (up to 24 hours) of an emergency department (ED) encounter. The secondary goal was to provide a framework for the operational implementation of these machine learning models.