Semantics Enhancing Knowledge Discovery and Ontology Engineering Using Mining Techniques

Knowledge Discovery and Data Mining ◽

10.4018/978-1-59904-252-7.ch009 ◽

2011 ◽

pp. 163-188

Author(s):

Elena Irina Neaga

Keyword(s):

Knowledge Discovery ◽

Knowledge Engineering ◽

Reference Model ◽

Distributed Processing ◽

Ontology Engineering ◽

Software Architectures ◽

Model Driven ◽

Architectural Models ◽

Definition Of ◽

Comprehensive Framework

This chapter deals with a roadmap on the bidirectional interaction and support between knowledge discovery (Kd) processes and ontology engineering (Onto) mainly directed to provide refined models using common methodologies. This approach provides a holistic literature review required for the further definition of a comprehensive framework and an associated meta-methodology (Kd4onto4dm) based on the existing theories, paradigms, and practices regarding knowledge discovery and ontology engineering as well as closely related areas such as knowledge engineering, machine/ontology learning, standardization issues and architectural models. The suggested framework may adhere to the Iso-reference model for open distributed processing and Omg-model-driven architecture, and associated dedicated software architectures should be defined.

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Advanced EHR Architectures–Promises or Reality

Methods of Information in Medicine ◽

10.1055/s-0038-1634044 ◽

2006 ◽

Vol 45 (01) ◽

pp. 95-101 ◽

Cited By ~ 10

Author(s):

B. G. M. E. Blobel

Keyword(s):

Information System ◽

Health Information ◽

Reference Model ◽

Distributed Processing ◽

Health Information System ◽

Generic Properties ◽

System Architectures ◽

Model Driven ◽

New Concepts ◽

New Generation

Summary Objectives: Forming the informational reflection of the patients and their care, the Electronic Health Record (EHR) is the core application of any complex health information system or health network. Such an ideally lifelong history file must be reliable, flexible, adaptable to new concepts and technologies, and robust, to allow for sharing knowledge over its lifetime. A sophisticated architecture must be chosen for meeting this challenge. Methods: An advanced EHR architecture for designing and implementing future-proof EHR systems must be a model of generic properties required for any Electronic Patient Record to provide communicable, comprehensive, useful, effective, and legally binding records that preserve their integrity over the time, independent of platforms and systems as well as of national special-ties. The resulting approach is based on the ISO Reference Model – Open Distributed Processing. Results: Based on advanced architectural principles introduced in the paper, a new generation of EHR systems has been designed and implemented for demonstrating the feasibility of the approach. This result is presented and evaluated regarding the achievements and problems using the component-based paradigm of model-driven health information system architectures. Conclusions: The future-proof EHR approach that has been established has been shortly evaluated. Advantages regarding flexibility, reliability, and portability of policy-driven, highly secure, role-dependent applications have to be considered in the light of performance as well as of the availability of network and application services.

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Development and Evaluation of Fuzzy Criteria for the Diagnosis of Rheumatoid Arthritis

Methods of Information in Medicine ◽

10.1055/s-0038-1634678 ◽

1996 ◽

Vol 35 (04/05) ◽

pp. 334-342 ◽

Cited By ~ 17

Author(s):

K.-P. Adlassnig ◽

G. Kolarz ◽

H. Leitich

Keyword(s):

Rheumatoid Arthritis ◽

Reference Model ◽

American Rheumatism Association ◽

Uniform Definition ◽

Sensitivity Rate ◽

Fuzzy Techniques ◽

Definition Of ◽

Different Levels ◽

Specificity Rate

Abstract:In 1987, the American Rheumatism Association issued a set of criteria for the classification of rheumatoid arthritis (RA) to provide a uniform definition of RA patients. Fuzzy set theory and fuzzy logic were used to transform this set of criteria into a diagnostic tool that offers diagnoses at different levels of confidence: a definite level, which was consistent with the original criteria definition, as well as several possible and superdefinite levels. Two fuzzy models and a reference model which provided results at a definite level only were applied to 292 clinical cases from a hospital for rheumatic diseases. At the definite level, all models yielded a sensitivity rate of 72.6% and a specificity rate of 87.0%. Sensitivity and specificity rates at the possible levels ranged from 73.3% to 85.6% and from 83.6% to 87.0%. At the superdefinite levels, sensitivity rates ranged from 39.0% to 63.7% and specificity rates from 90.4% to 95.2%. Fuzzy techniques were helpful to add flexibility to preexisting diagnostic criteria in order to obtain diagnoses at the desired level of confidence.

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