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

Designing a Framework to Develop eLoyalty for Online Healthcare Services  pp107-115

Marcelina Solano-Lorente, Eva Martinez-Caro, Juan Gabriel Cegarra-Navarro

© Jan 2013 Volume 11 Issue 1, ECKM 2012, Editor: Dr Juan Gabriel Cegarra and Dr María Eugenia Sánchez, pp1 - 115

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Abstract

In an attempt to enhance their effectiveness, public health institutions are making a great effort to develop online health care services targeted to all citizens. The initial acceptance of these services by patients is important but their success depends

 

Keywords: eLoyalty, satisfaction, technology acceptance model, health care, patient, information system success models

 

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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 treatm ents. 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 an d 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 Issue

Volume 11 Issue 1, ECKM 2012 / Jan 2013  pp1‑115

Editor: Dr Juan Gabriel Cegarra, Dr María Eugenia Sánchez

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Editorial

The Papers in this issue of EJKM were first presented at the European Conference of Knowledge Management.

 

The issue was edited by the Programme Chair Dr Juan Gabriel Cegarra and the Conference Chair Dr María Eugenia Sánchez

 

 


Juan‑Gabriel‑Cegarra   Maria‑Eugenia‑Sanchez
                
  Dr Juan Gabriel Cegarra            Dr María Eugenia Sánchez

 

 

Keywords: global social knowledge management, social software, barriers, distributed teamwork, contextualization, cultural influence, cognitive knowledge, emotional knowledge, knowledge dynamics, microexpressions, negotiations, social capital, familiness, power, experience, innovation, family business, knowledge sharing, theory of planned behaviour, affective commitment and trust, scientific collaboration, collaboratory, knowledge audit, knowledge management, scientific collaboration recommendation, knowledge base, innovation outcome, cultural barriers, healthcare organizations, phase, shipyard, naval industry, relationship, reutilization, internal and external agents, intellectual capital management, ICM, knowledge, biplot, knowledge, knowledge management, knowledge assessment framework, case study, eLoyalty, satisfaction, technology acceptance model, health care, patient, information system success models

 

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