{"title":"The Closest Incomplete Medical Information Systems","authors":"Katarzyna Ignatiuk, A. Dardzinska","doi":"10.1109/BIOMDLORE.2018.8467192","DOIUrl":null,"url":null,"abstract":"One of the main problem of medical information systems are incompleteness that may be caused by unreliable supplemented patient documentation, loss of part of documentation or errors during transferring information from paper to electronic documentation. Information gaps can be replaced by values suggested by statistical or rule-based methods or with the help of other information systems working in the same ontology. Each system may have a different set of rules but referring to objects described by the same set of attributes. Medical information systems communicate each other to find an answer: “How treatment to take for patient described by given set of attributes?” or “What value should have the missing attribute so that the patient can be moved from one to another decision class?” Sometimes, the responses from different systems may be supported by insufficient number of rules or rules can be in conflict. Therefore, it is important to properly assess which systems should collaborate each other to answer the query asked to the incomplete system. This work will present a method of assessing collaboration between systems.","PeriodicalId":151729,"journal":{"name":"2018 International Conference BIOMDLORE","volume":"124 15 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 International Conference BIOMDLORE","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/BIOMDLORE.2018.8467192","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2
Abstract
One of the main problem of medical information systems are incompleteness that may be caused by unreliable supplemented patient documentation, loss of part of documentation or errors during transferring information from paper to electronic documentation. Information gaps can be replaced by values suggested by statistical or rule-based methods or with the help of other information systems working in the same ontology. Each system may have a different set of rules but referring to objects described by the same set of attributes. Medical information systems communicate each other to find an answer: “How treatment to take for patient described by given set of attributes?” or “What value should have the missing attribute so that the patient can be moved from one to another decision class?” Sometimes, the responses from different systems may be supported by insufficient number of rules or rules can be in conflict. Therefore, it is important to properly assess which systems should collaborate each other to answer the query asked to the incomplete system. This work will present a method of assessing collaboration between systems.