{"title":"医院内疾病的建模与仿真方法","authors":"Diego Encinas, Lucas Maccallini, Fernando Romero","doi":"10.24215/16666038.21.e14","DOIUrl":null,"url":null,"abstract":"This publication presents an approach to a simulator to recreate a large number of scenarios and to make agile decisions in the planning of a real emergency room system. A modeling and simulation focused on the point prevalence of intrahospital infections in an emergency room and how it is affected by different factors related to hospital management. To carry out the simulator modeling, the Agent-based Modeling and Simulation (ABMS) paradigm was used. Thus, different intervening agents in the emergency room environment — patients and doctors, among others— were classified. The user belonging to the health system has different data to configure the simulation, such as the number of patients, the number of available beds, etc. \nBased on the tests carried out and the measurements obtained, it is concluded that the disease propagation model relative to the time and contact area of the patients has greater precision than the purely statistical model of the intensive care unit.","PeriodicalId":188846,"journal":{"name":"J. Comput. Sci. Technol.","volume":"33 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"An Approach to the Modeling and Simulation of Intra-Hospital Diseases\",\"authors\":\"Diego Encinas, Lucas Maccallini, Fernando Romero\",\"doi\":\"10.24215/16666038.21.e14\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This publication presents an approach to a simulator to recreate a large number of scenarios and to make agile decisions in the planning of a real emergency room system. A modeling and simulation focused on the point prevalence of intrahospital infections in an emergency room and how it is affected by different factors related to hospital management. To carry out the simulator modeling, the Agent-based Modeling and Simulation (ABMS) paradigm was used. Thus, different intervening agents in the emergency room environment — patients and doctors, among others— were classified. The user belonging to the health system has different data to configure the simulation, such as the number of patients, the number of available beds, etc. \\nBased on the tests carried out and the measurements obtained, it is concluded that the disease propagation model relative to the time and contact area of the patients has greater precision than the purely statistical model of the intensive care unit.\",\"PeriodicalId\":188846,\"journal\":{\"name\":\"J. Comput. Sci. Technol.\",\"volume\":\"33 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-10-21\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"J. Comput. Sci. Technol.\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.24215/16666038.21.e14\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"J. Comput. Sci. Technol.","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.24215/16666038.21.e14","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
An Approach to the Modeling and Simulation of Intra-Hospital Diseases
This publication presents an approach to a simulator to recreate a large number of scenarios and to make agile decisions in the planning of a real emergency room system. A modeling and simulation focused on the point prevalence of intrahospital infections in an emergency room and how it is affected by different factors related to hospital management. To carry out the simulator modeling, the Agent-based Modeling and Simulation (ABMS) paradigm was used. Thus, different intervening agents in the emergency room environment — patients and doctors, among others— were classified. The user belonging to the health system has different data to configure the simulation, such as the number of patients, the number of available beds, etc.
Based on the tests carried out and the measurements obtained, it is concluded that the disease propagation model relative to the time and contact area of the patients has greater precision than the purely statistical model of the intensive care unit.