首页|Studies from Catholic University of Korea Describe New Findings in Machine Learn ing (Enhancing Patient Flow in Emergency Departments: A Machine Learning and Sim ulation-Based Resource Scheduling Approach)
Studies from Catholic University of Korea Describe New Findings in Machine Learn ing (Enhancing Patient Flow in Emergency Departments: A Machine Learning and Sim ulation-Based Resource Scheduling Approach)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on artificial intelligenc e is the subject of a new report. According to news reporting from Seoul, South Korea, by NewsRx journalists, research stated, “The efficient scheduling of reso urces within emergency departments (EDs) is crucial to minimizing patient length of stay (LoS) times and maximizing the utilization of limited resources.” Financial supporters for this research include National Research Foundation of K orea. Our news journalists obtained a quote from the research from Catholic University of Korea: “Reducing patient wait times can enhance the operation of emergency d epartments and improve patient satisfaction and the quality of medical care. Thi s study develops a simulation model using Discrete Event Simulation (DES) method ology, examining six resource scheduling policies that consider different combin ations of general and senior physicians. By leveraging six scheduling policies a nd machine learning techniques, this model dynamically identifies the most effec tive scheduling policy, based on a comprehensive dataset of ED visits in South K orea.”
Catholic University of KoreaSeoulSou th KoreaAsiaCyborgsEmerging TechnologiesMachine Learning
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