The Electronic Journal of Knowledge Management publishes original articles on topics relevant to studying, implementing, measuring and managing knowledge management and intellectual capital.

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Journal Article

Mining Knowledge of the Patient Record: The Bayesian Classification to Predict and Detect Anomalies in Breast CancerŽ  pp128-139

Souad Demigha

© Aug 2016 Volume 14 Issue 3, Editor: Vincent Ribiere, pp113 - 190

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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 Translation in Oncology. A Case Study  pp212-223

Francesca Dal Mas, Helena Biancuzzi, Maurizio Massaro, Amelia Barcellini, Lorenzo Cobianchi, Luca Miceli

© Jan 2020 Volume 18 Issue 3, Editor: Maria José Sousa, Renato Lopes da Costa and Francesca Dal Mas, pp198 - 235

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Abstract

Knowledge translation (KT) is the ability to make knowledge accessible to different stakeholders by translating it into various contexts. Translating knowledge is particularly crucial in the healthcare sector, which is currently under significant pressure due to technological innovation, increasing demand of services by an ageing population, budget reductions, and new organisational challenges posed by the latest events like the COVID‑19 pandemic. While the first definition of KT was focused on the translation of scientific research into clinical practice, other types of KT later emerged. In healthcare, while stakeholders have different skills and competencies (such as clinical scientists versus physicians or other healthcare professionals), others experience diverse emotional feelings (like the patients or their families). An effective KT allows the transfer, sharing, and creation of new knowledge, enhancing innovation and co‑production dynamics. The paper employs a case study by analysing the Breast Unit of the C.R.O. National Cancer Institue of Aviano, Italy, one of the most acknowledged hospitals and research centres in Europe in the field of cancer surgery and treatments. The paper aims at studying the knowledge translation dynamics and tools by analysing the various relationships with the internal as well as the external stakeholders of the Breast Unit. Internally, knowledge translation is needed to merge the competencies of highly skilled multidisciplinary teams, which include surgeons and physicians with various specialities, researchers, psychologists, nurses and other healthcare professionals. Externally, knowledge is translated to meet the needs of patients, patients' associations, sponsors, citizens, and policymakers. Results highlight how different techniques and dynamics allow KT to happen within internal as well as external groups. Contributing to the knowledge management and knowledge translation theories, our findings open up to practical as well as research implications.

 

Keywords: Translation, Healthcare, Stakeholders, Breast Cancer, Medicine

 

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