An Application of Deep Reinforcement Learning for Ambulance Allocation to Emergency Departments under Overcrowding Situation, M.Sc. Thesis Sharif University of Technology ; Akhavan Niaki, Taghi (Supervisor)
Abstract
In the last decade, emergency department (ED) overcrowding has become a national crisis for the US healthcare system. Increasing mortality rates, decreasing quality of care, financial losses due to walkouts, and ambulance diversion are some of the consequences of ED overcrowding. Given the increasing demand in terms of ambulance utilization which we can see an instance of it in the COVID-19 pandemic, being able to allocate service requests to EDs efficiently, becomes a key function of emergency medical services. in this investigation, an algorithm of deep reinforcement learning called deep Q-learning is used to address this problem and to assign ambulances to ED's appropriately. under...
Cataloging briefAn Application of Deep Reinforcement Learning for Ambulance Allocation to Emergency Departments under Overcrowding Situation, M.Sc. Thesis Sharif University of Technology ; Akhavan Niaki, Taghi (Supervisor)
Abstract
In the last decade, emergency department (ED) overcrowding has become a national crisis for the US healthcare system. Increasing mortality rates, decreasing quality of care, financial losses due to walkouts, and ambulance diversion are some of the consequences of ED overcrowding. Given the increasing demand in terms of ambulance utilization which we can see an instance of it in the COVID-19 pandemic, being able to allocate service requests to EDs efficiently, becomes a key function of emergency medical services. in this investigation, an algorithm of deep reinforcement learning called deep Q-learning is used to address this problem and to assign ambulances to ED's appropriately. under...
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