The Closest Incomplete Distributed Information System for Medical Query Answering System
The common issue for medical information systems are missing values. Generally, gaps are filled by statistically suggested values or rule-based methods. Another approach is to use the knowledge of information systems working under the same ontology. The medical incomplete system receives a query una...
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doaj-9c496a9fc7fa4fedadd25b01bf0a935b2021-09-06T19:41:06ZengSciendoActa Mechanica et Automatica 2300-53192018-06-0112216016410.2478/ama-2018-0025ama-2018-0025The Closest Incomplete Distributed Information System for Medical Query Answering SystemIgnatiuk Katarzyna0Dardzińska Agnieszka1Department of Biomechanics and Biomedical Engineering, Bialystok University of Technology, ul. Wiejska 45c, 15-351Białystok, PolandDepartment of Biomechanics and Biomedical Engineering, Bialystok University of Technology, ul. Wiejska 45c, 15-351Białystok, PolandThe common issue for medical information systems are missing values. Generally, gaps are filled by statistically suggested values or rule-based methods. Another approach is to use the knowledge of information systems working under the same ontology. The medical incomplete system receives a query unable to answer, because of some unknown patient attributes. So, it has to communicate with other medical systems. The result of the collaboration is collective knowledgebase. In this paper, we propose a measure supporting choice of closest pair of systems. It determines the distance between the two systems. We use ERID algorithm to extract rules from incomplete, distributed information systems. Each constructed rule has confidence and support. They allowed to determine the distance between a pair of medical information systems. The proposed solution was verified on the basis of several “manipulated” medical information systems. Next, the solution was verified in systems with randomly selected data. The satisfying results were obtained and based on them, the proposed measure can be successfully used in medical systems to support the work of doctors and the treatment of patients.https://doi.org/10.2478/ama-2018-0025query answering systemknowledge extractionincomplete medical information systemdistributed medical information systemthe closest information system |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Ignatiuk Katarzyna Dardzińska Agnieszka |
spellingShingle |
Ignatiuk Katarzyna Dardzińska Agnieszka The Closest Incomplete Distributed Information System for Medical Query Answering System Acta Mechanica et Automatica query answering system knowledge extraction incomplete medical information system distributed medical information system the closest information system |
author_facet |
Ignatiuk Katarzyna Dardzińska Agnieszka |
author_sort |
Ignatiuk Katarzyna |
title |
The Closest Incomplete Distributed Information System for Medical Query Answering System |
title_short |
The Closest Incomplete Distributed Information System for Medical Query Answering System |
title_full |
The Closest Incomplete Distributed Information System for Medical Query Answering System |
title_fullStr |
The Closest Incomplete Distributed Information System for Medical Query Answering System |
title_full_unstemmed |
The Closest Incomplete Distributed Information System for Medical Query Answering System |
title_sort |
closest incomplete distributed information system for medical query answering system |
publisher |
Sciendo |
series |
Acta Mechanica et Automatica |
issn |
2300-5319 |
publishDate |
2018-06-01 |
description |
The common issue for medical information systems are missing values. Generally, gaps are filled by statistically suggested values or rule-based methods. Another approach is to use the knowledge of information systems working under the same ontology. The medical incomplete system receives a query unable to answer, because of some unknown patient attributes. So, it has to communicate with other medical systems. The result of the collaboration is collective knowledgebase. In this paper, we propose a measure supporting choice of closest pair of systems. It determines the distance between the two systems. We use ERID algorithm to extract rules from incomplete, distributed information systems. Each constructed rule has confidence and support. They allowed to determine the distance between a pair of medical information systems. The proposed solution was verified on the basis of several “manipulated” medical information systems. Next, the solution was verified in systems with randomly selected data. The satisfying results were obtained and based on them, the proposed measure can be successfully used in medical systems to support the work of doctors and the treatment of patients. |
topic |
query answering system knowledge extraction incomplete medical information system distributed medical information system the closest information system |
url |
https://doi.org/10.2478/ama-2018-0025 |
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