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Biomedical subjects

Peter Kokol

Publications and source records attributed to Peter Kokol.

3 recordsLinked to original sources

e-Learning in nursing education--Challenges and opportunities.

Quick changes on the field of informational communication technologies forces educational and other institutions to think about different ways of teaching and learning in both formal and informal environments. It addition it is well known that due to fast advancement of science and technology the knowledge gained in schools is getting out-of-date rapidly, so life long learning is becoming an essential alternative. As a consequence we are facing a rapid development and use of new educational approaches such as e-learning, simulations, virtual reality, etc. They brought a revolution to learning and instruction. But in general the empirical results of e-learning studies are somewhat disappointing. They cannot prove the superiority of e-learning processes over traditional learning in general, neither in specific areas like nursing. In our international study we proved that e-Learning can have many benefits and that it can enhance learning experience in nursing education, but it has to be provided in correct manner.

Education, Distance↗

Knowledge discovery with classification rules in a cardiovascular dataset.

In this paper we study an evolutionary machine learning approach to data mining and knowledge discovery based on the induction of classification rules. A method for automatic rules induction called AREX using evolutionary induction of decision trees and automatic programming is introduced. The proposed algorithm is applied to a cardiovascular dataset consisting of different groups of attributes which should possibly reveal the presence of some specific cardiovascular problems in young patients. A case study is presented that shows the use of AREX for the classification of patients and for discovering possible new medical knowledge from the dataset. The defined knowledge discovery loop comprises a medical expert's assessment of induced rules to drive the evolution of rule sets towards more appropriate solutions. The final result is the discovery of a possible new medical knowledge in the field of pediatric cardiology.

Algorithms↗

Decision trees: an overview and their use in medicine.

In medical decision making (classification, diagnosing, etc.) there are many situations where decision must be made effectively and reliably. Conceptual simple decision making models with the possibility of automatic learning are the most appropriate for performing such tasks. Decision trees are a reliable and effective decision making technique that provide high classification accuracy with a simple representation of gathered knowledge and they have been used in different areas of medical decision making. In the paper we present the basic characteristics of decision trees and the successful alternatives to the traditional induction approach with the emphasis on existing and possible future applications in medicine.

Clinical Medicine↗