The Electronic Journal of Knowledge Management publishes perspectives on topics relevant to the study, implementation and management of knowledge management
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Journal Article

Mining Knowledge of the Patient Record: The Bayesian Classification to Predict and Detect Anomalies in Breast CancerŽ  pp127-138

Souad Demigha

© Aug 2016 Volume 14 Issue 3, Following ICICKM, Editor: Vincent Ribiere, pp113 - 188

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Abstract

Abstract: knowledge management, data mining, and text mining techniques have been adopted in various successful biomedical applications in recent years. Data Mining (DM) is the most important subfields in knowledge management (KM). It has been proven that data mining can enhance the KM process with better knowledge. In this paper, we investigate the application of DM techniques for mining knowledge of the patient record. The patient record represents documents of the patients examinations and treatments. Data Mining is the process of miningŽ or extracting information from a data set and transform it into an understandable structure for further use. We propose a methodology for mining medical knowledge based on the Bayesian Classification to predict and detect anomalies in breast cancer. We use the Naïve Bayes Algorithm to develop this methodology. We illustrate the knowledge mining process by real examples of medical field. We investigate through these illustrations how knowledge is better mined and thus, reused when applying concepts and techniques of Data Mining. On the other hand, we investigate the potential contribution of the Naive Bayesian Classification methodology as a reliable support in computer‑aided diagnosis of such events, using the well‑known Wisconsin Prognostic Breast Cancer dataset. Finally, we will demonstrate the suitability and ability of the Naive Bayes methodology in Classification/Prediction problems in breast cancer.

 

Keywords: Keywords: Patient Record, Data Mining, Bayesian Classification, Naïve Bayes Algorithm, Breast cancer prediction

 

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Journal Article

Knowledge Management System Building Blocks  pp137-148

Georg Hüttenegger

© Nov 2003 Volume 1 Issue 2, Editor: Fergal McGrath, pp1 - 226

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Abstract

This paper describes three building blocks of a technological Knowledge Management (KM) system that provides all relevant and practical means of supporting KM and thus differentiates itself from existing KM tools in goal and approach, as they usually deal with a limited range only. The three blocks described within this paper are: a virtual information pool, which utilizes Enterprise Application Integration (EAI), a single and central user interface providing ubiquitous access, and mechanisms to enrich the available data, essentially based on Artificial Intelligence and Data Mining techniques.

 

Keywords: Knowledge Management System, Virtual Information Pool, Ubiquitous Access, Machine Learning Data Mining

 

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Journal Article

Data Mining as a Technique for Knowledge Management in Business Process Redesign  pp33-44

Olusegun Folorunso, Adewale O. Ogunde

© Jan 2005 Volume 2 Issue 1, Editor: Charles Despres, pp1 - 90

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Abstract

Business Process Redesign (BPR) is undertaken to achieve order‑of‑magnitude improvements over 'old' form of the organisation. Practitioners in the academia and business world have developed a number of methodologies to support this competitive restructuring that forms the current focus of concern, many of which have not been successful. This paper suggests the use of Data Mining (DM) as a technique to support the process of redesigning a business by extracting the much‑needed knowledge hidden in large volumes of data maintained by the organization through the DM models.

 

Keywords: Data Mining, Knowledge Management, Business Process Redesign, Business reengineering, Artificial Neural Networks

 

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