{"title":"Risk Analysis of Operating Room Using the Fuzzy Bayesian Network Model","authors":"B. Zoullouti, M. Amghar, N. Sbiti","doi":"10.5829/idosi.ije.2017.30.01a.09","DOIUrl":null,"url":null,"abstract":"To enhance Patient’s safety, we need effective methods for risk management. This work aims to propose an integrated approach to risk management for a hospital system. To improve patient’s safety, we should develop flexible methods where different aspects of risk and type of information are taken into consideration. This paper proposes a fuzzy Bayesian network to model and analyze risk in the operating room. Bayesian networks provide a framework for presenting causal relationships and enable probabilistic inference among a set of variables. Fuzzy logic allows using the expert’s opinions when quantitative data are lacking and only qualitative or vague statements can be made. This approach provides an actionable model that accurately supports human cognition using linguistic variables. A case study of the patient’s safety risk is used to illustrate the application of the proposed method.","PeriodicalId":416886,"journal":{"name":"International journal of engineering. Transactions A: basics","volume":"19 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2016-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"9","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International journal of engineering. Transactions A: basics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.5829/idosi.ije.2017.30.01a.09","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 9
Abstract
To enhance Patient’s safety, we need effective methods for risk management. This work aims to propose an integrated approach to risk management for a hospital system. To improve patient’s safety, we should develop flexible methods where different aspects of risk and type of information are taken into consideration. This paper proposes a fuzzy Bayesian network to model and analyze risk in the operating room. Bayesian networks provide a framework for presenting causal relationships and enable probabilistic inference among a set of variables. Fuzzy logic allows using the expert’s opinions when quantitative data are lacking and only qualitative or vague statements can be made. This approach provides an actionable model that accurately supports human cognition using linguistic variables. A case study of the patient’s safety risk is used to illustrate the application of the proposed method.