RESEARCH PAPER
The Closest Incomplete Distributed Information System for Medical Query Answering System
1
Department of Biomechanics and Biomedical Engineering, Bialystok University of Technology, ul. Wiejska 45c, 15-351 Białystok, Poland
Submission date: 2017-09-29
Acceptance date: 2018-06-30
Online publication date: 2018-07-17
Publication date: 2018-06-01
Acta Mechanica et Automatica 2018;12(2):160-164
KEYWORDS
Query Answering SystemKnowledge ExtractionIncomplete Medical Information SystemDistributed Medical Information SystemThe Closest Information System
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.
REFERENCES (17)
1.
Dardzinska A. (2004), Null Values and Chase in Distributed Information System, Knowledge-Based Intelligent Information and Engineering Systems, Springer, 1143–1149.
3.
Dardzinska A., Ignatiuk K., Zdrodowska M. (2017), Query Answering System as a Tool in Incomplete Distributed Information System Optimization Process, Proceedings of FDSE 2017, HoChi Minh City, Vietnam, 101–109.
4.
Dardzinska A., Ras Z. (2003), CHASE2 - Rules Based Chase Algorithm for Information Systems of Type λ, Active Mining, Springer-Verlag, 255–267.
5.
Dardzinska A., Ras Z. (2006), Extracting Rules from Incomplete Decision Systems: System ERID, Foundations and Novel Approaches in Data Mining, Springer, 143–153.
6.
Guarino N. (1998), Formal Ontology in Information Systems, Proceedings of FOIS’98, Trento, Italy, 3–15.
7.
Guarino N., Giaretta P. (1995), Ontologies and knowledge bases, towards a terminological clarification, Towards Very Large Knowledge Bases: Knowledge Building and Knowledge Sharing, 25–32.
8.
Laudon K., Laudon J. (2012), Management Information System: Managing the Digital Firm, Prentice Hall, New Jersey, 14–16.
9.
Mizoguchi R. (2003), Tutorial on ontological engineering - Part 1: Introduction to Ontological Engineering, New Generation Computing, 21 (4), 365–384.
10.
Ras Z. (1997), Collaboration control in distributed knowledge-based system, Information Sciences, 96 (3), 193–205.
11.
Ras Z. (2001), Query answering based on distributed knowledge mining, Proceedings of the 2nd Asia-Pacific Conference on Intelligent Agent Technology: Research and Development, Maebashi City, Japan, 17–27.
12.
Ras Z. (2002), Reducts-driven query answering for distributed knowledge systems, International Journal of Intelligent Systems, 17 (2), 113–124.
13.
Ras Z., Dardzinska A. (2006), Solving Failing Queries through Cooperation and Collaboration, World Wide Web Journal, 9 (2), 173–186.
14.
Ras Z., Dardzinska A. (2009), Cooperative Multi-hierarchical Query Answering Systems, Encyclopedia of Complexity and Systems Science, Springer, New York, 1532–1537.
15.
Ras Z., Joshi S. (1997), Query approximate answering system for an incomplete DKBS, Foundamenta Informaticae Journal, 30 (3), 313–324.
16.
Van Heijst G., Schreiber A., Wielinga B. (1997), Using explicit ontologies in KBS development, International Journal of Human and Computer Studies. 46 (2), 183–292.
17.
Yoo I., Alafaireet P., Marinov M., Pena-Hernandez K., Gopidi R., Chang J., Hua L. (2012), Data mining in healthcare and bio-medicine: A survey of the literature, Journal of Medical Systems, 36 (4), 2431–2448.
| eISSN: | 2300-5319 |
| ISSN: | 1898-4088 |
We process personal data collected when visiting the website. The function of obtaining information about users and their behavior is carried out by voluntarily entered information in forms and saving cookies in end devices. Data, including cookies, are used to provide services, improve the user experience and to analyze the traffic in accordance with the Privacy policy. Data are also collected and processed by Google Analytics tool (more).
You can change cookies settings in your browser. Restricted use of cookies in the browser configuration may affect some functionalities of the website.
You can change cookies settings in your browser. Restricted use of cookies in the browser configuration may affect some functionalities of the website.
