Emergency Medical Service System Simulation Based On Emergency Scenes

Emergency medical service system is the most important part in theemergency, its role is to reduce the losses of property and casualtiescaused by the unexpected incidents. It is a useful system in theemergency scenes for maintenance of social stability and development.The study of medical emergency system can help the medical departmentto optimize the organization and improve the efficiency. Based on theanalysis of the review of the emergency medical service, the medicalwarning service system and the hospital emergency response system arestudied. For the research of emergency medical service system is notperfect, the article analyzes the two main components of the emergencymedical system, and presents a flu prediction model based on intervalprediction and the hospital resources allocation and optimization modelbased on stochastic programming.Based on the research of the emergency medical service system, theimportant of the medical warning service system is analyzed to thereduction of the incidents, the monitor and the control of the public healthincidents. But the research of the emergency medical service system isnot perfect, the article present a influenza trends forecast based on the interval prediction model. The tradition point forecast is a randomvariable based on the estimate the expected value. It is not contain theconfidence level and the prediction interval width which are useful for thedecision-makers to confirm the Influenza Trends and make more effectivedecisions. To solve this problem, the neural network lower upper boundestimation method（LUBE）which builds prediction interval（sPI）is usedto develop Influenza Trends interval prediction model in this paper, thecombinational coverage width-based criterion (CWC) is proposed toevaluate the prediction intervals, the Simulated Annealing Algorithm isused to train the model, the model is simulated by the real emergencydata. To assess the performance of prediction interval, the model iscompared with other models. The result appears that the SimulatedAnnealing Algorithm neural network interval prediction model caneffectively predict Influenza Trends.Based on the research of the emergency medical service system, thehospital emergency response system is studied, it is not found that thehospital resource allocation optimization model which combine thepatient health random status. The process of the emergency plan isanalyzed, based on the multiple incidents, the random types of patientsarriving in the hospital randomly, the hospital treat the patient based onthe flow process of the emergency plan randomly. The key elements andthe random relationship of them is studied, and the dynamics random model of hospital response system is build. According to this model, theimpact of patients’ health status changes to the system is analyzed, Healthresources allocation is not reasonable cause can not get effective use ofmedical resources. So according combined with the dynamic randomsimulation model and the Ant Colony Algorithm, a hospital responsesystem resource allocation and optimization model based on thestochastic programming is build. This model can help the hospital to getan optimized solution of the resource allocation based on the patients’random arrivals, the model provide a useful method to the hospitalevaluation system.