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Knowledge-Based Data Analysis: First Step Toward the Creation of Clinical Prediction Rules Using a New Typicality Measure

Content Provider	IEEE Xplore Digital Library
Author	Kwiatkowska, M. Atkins, M.S. Ayas, N.T. Ryan, C.F.
Copyright Year	1997
Abstract	Clinical prediction rules play an important role in medical practice. They expedite diagnosis and limit unnecessary tests. However, the rule creation process is time consuming and expensive. With the current developments of efficient data mining algorithms and growing accessibility to medical data, the creation of clinical rules can be supported by automated rule induction from data. A data-driven method based on the reuse of previously collected medical records and clinical trial statistics is cost-effective; however, it requires well defined and intelligent methods for data analysis. This paper presents a new framework for knowledge representation for secondary data analysis and for generation of a new typicality measure, which integrates medical knowledge into statistical analysis. The framework is based on a semiotic approach for contextual knowledge and fuzzy logic for approximate knowledge. This semio-fuzzy framework has been applied to the analysis of predictors for the diagnosis of obstructive sleep apnea. This approach was tested on two clinical data sets. Medical knowledge was represented by a set of facts and fuzzy rules, and used to perform statistical analysis. Statistical methods provided several candidate outliers. Our new typicality measure identified those, which were medically significant, in the sense that the removal of those important outliers improved the descriptive model. This is a critical preprocessing step towards automated induction of predictive rules from data. These experimental results demonstrate that knowledge-based methods integrated with statistical approaches provide a practical framework to support the generation of clinical prediction rules.
Sponsorship	IEEE Engineering in Medicine and Biology Society IEEE Computer Society
Page Count	10
File Size	639179
Starting Page	651
Ending Page	660
File Format	PDF
ISSN	10897771
Volume Number	11
Issue Number	6
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2007-11-01
Publisher Place	U.S.A.
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Data analysis Medical diagnostic imaging Statistical analysis Testing Induction generators Data mining Clinical trials Knowledge representation Fuzzy logic Sleep apnea typicality measure Clinical prediction rules (CPRs) data analysis fuzzy logic obstructive sleep apnea (OSA)
Content Type	Text
Resource Type	Article
Subject	Electrical and Electronic Engineering Computer Science Applications Biotechnology

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