Classifying Emergency Patients into Fast-Track and Complex Cases using Machine Learning

Abstract

Emergency medicine is a lifeline specialty at hospitals that patients head to for various reasons, including serious health problems, traumas, and adventitious conditions. Emergency departments are restricted to limited resources and personnel, complicating the optimal handling of all received cases. Therefore, crowded waiting areas and long waiting durations result. In this research, the databases of MIMIC-IV-ED and MIMIC-IV were utilized to obtain records of patients who visited the Beth Israel Deaconess Medical Center in the USA. Triage data, dispositions, and length of stay of these individuals were extracted. Subsequently, the urgency of these cases was inferred based on standards stated in the literature and followed in developed countries. A comparative framework using four different machine learning algorithms besides a reference model was developed to classify these patients into complex and fast–track categories. Moreover, the relative importance of employed predictors was determined. This study proposes an approach to deal with non-urgent visits and lower overall waiting times at the emergency by utilizing the powers of machine learning to identify high-severity and low-severity patients. Given the provision of the required resources, the proposed classification would help improve the overall throughput and patient satisfaction.