Title
Consistent knowledge discovery in medical diagnosis
Document Type
Article
Department or Administrative Unit
Computer Science
Publication Date
7-2000
Abstract
Discusses eliminating contradictions among rules in computer-aided systems, experts rules, and databases. The study has demonstrated how consistent data mining in medical diagnosis can create a set of logical diagnostic rules for computer-aided diagnostic systems. Consistency avoids contradiction among rules generated using data mining software, rules used by an experienced radiologist, and a database of pathologically confirmed cases. The authors identified major problems: to find contradiction between diagnostic rules and to eliminate contradiction. They applied two complimentary intelligent technologies for extraction of rules and recognition of their contradictions. The first technique is based on discovering statistically significant logical diagnostic rules. The second technique is based on the restoration of a monotone Boolean function to generate a minimal dynamic sequence of questions to a medical expert. The results of this mutual verification of expert and data-driven rules demonstrate feasibility of the approach for designing consistent computer-aided diagnostic systems.
Recommended Citation
Kovalerchuk, B., Vityaev, E., & Ruiz, J. F. (2000). Consistent knowledge discovery in medical diagnosis. IEEE Engineering in Medicine and Biology Magazine, 19(4), 26–37. https://doi.org/10.1109/51.853479
Journal
IEEE Engineering in Medicine and Biology Magazine
Rights
Copyright © 2000, IEEE
Comments
This article was originally published in IEEE Engineering in Medicine and Biology Magazine. The article from the publisher can be found here.
Due to copyright restrictions, this article is not available for free download from ScholarWorks @ CWU.