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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Main Authors: Ignatiuk Katarzyna, Dardzińska Agnieszka
Format: Article
Language:English
Published: Sciendo 2018-06-01
Series:Acta Mechanica et Automatica
Subjects:
Online Access:https://doi.org/10.2478/ama-2018-0025
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spelling 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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