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Constructing an initial knowledge base for medical domain expert system using induct RDR


Hyeon, J and Oh, K-J and Kim, YJ and Chung, H and Kang, BH and Choi, H-J, Constructing an initial knowledge base for medical domain expert system using induct RDR, Proceedings of the 2016 International Conference on Big Data and Smart Computing (BigComp), 18-20 January 2016, Hong Kong, China, pp. 408-410. ISBN 978-1-4673-8796-5 (2016) [Refereed Conference Paper]


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Copyright 2016 IEEE

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DOI: doi:10.1109/BIGCOMP.2016.7425958


This paper describes how we build an initial knowledge-base of ripple-down rules (RDR) in medical domain. In medical domain, all decisions are made by the domain experts. Increasing a complexity of disease and various symptoms, there are some attempts to introduce an expert system in medical domain these days. To construct the expert system, it needs to extract the expert's knowledge. To do that, we use ripple-down rules (RDR) which allows experts to modify their knowledge base directly because it provides a systematic approach to do that. We also use Induct RDR which builds a knowledge base from existing data to reduce experts' burden of adding their knowledge from the bottom up. The expert system should produce multiple comments from a test set, which is multiple classification problem. However, Induct RDR only deals with a single classification problem. To handle this problem, we divide a test set into 18 categories which is almost the single classification problem and apply Induct RDR to each category independently. Using this approach, we can improve the missing rate about 70% compared to an approach not dividing into several categories.

Item Details

Item Type:Refereed Conference Paper
Keywords:knowledge base, medical domain, ripple-down rules, induct RDR, multiple classification problem
Research Division:Health Sciences
Research Group:Health services and systems
Research Field:Health informatics and information systems
Objective Division:Information and Communication Services
Objective Group:Information systems, technologies and services
Objective Field:Information systems, technologies and services not elsewhere classified
UTAS Author:Chung, H (Mr David Chung)
UTAS Author:Kang, BH (Professor Byeong Kang)
ID Code:116904
Year Published:2016
Deposited By:Office of the School of Engineering
Deposited On:2017-05-24
Last Modified:2022-06-17
Downloads:201 View Download Statistics

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