<abstract xmlns="http://www.w3.org/1999/xhtml"><p>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.</p></abstract>

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.

The Closest Incomplete Distributed Information System for Medical Query Answering System

Department of Biomechanics and Biomedical Engineering

Acta Mechanica et Automatica

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

The common issue for medical information systems are missing values. Generally, gaps are filled by statistically suggested values or rule-based methods....

The Closest Incomplete Distributed Information System for Medical Query Answering System

Published Online: Jul 17, 2018

Page range: 160 - 164

Received: Sep 29, 2017

Accepted: Jun 30, 2018

DOI: https://doi.org/10.2478/ama-2018-0025

Keywords
Query Answering System, Knowledge Extraction, Incomplete Medical Information System, Distributed Medical Information System, The Closest Information System

© 2018 Katarzyna Ignatiuk et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

The Closest Incomplete Distributed Information System for Medical Query Answering System

Published Online: Jul 17, 2018

Page range: 160 - 164

Received: Sep 29, 2017

Accepted: Jun 30, 2018

DOI: https://doi.org/10.2478/ama-2018-0025

KeywordsQuery Answering System, Knowledge Extraction, Incomplete Medical Information System, Distributed Medical Information System, The Closest Information System

© 2018 Katarzyna Ignatiuk et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Keywords
Query Answering System, Knowledge Extraction, Incomplete Medical Information System, Distributed Medical Information System, The Closest Information System