Associated positive patch test reactions to standard contact allergens: 10-year data from the Slovenian E-Surveillance System.

Background: A considerable share of patients tested with a baseline patch test series respond with a positive reaction to more than one allergen, and some associations between synchronous positive reactions to distinct baseline patch allergens have been described in the literature.

Objectives: To evaluate the prevalence of sensitization to haptens of the European baseline series as well as the prevalence of oligosensitization and polysensitization and the most significant associated positive patch test reactions in Slovenia.

Methods: Patch testing data collected by the Slovenian E-Surveillance System from January 2008 to December 2017 were retrospectively analysed.

Datasets for phishing websites detection.

Phishing stands for a fraudulent process, where an attacker tries to obtain sensitive information from the victim. Usually, these kinds of attacks are done via emails, text messages, or websites. Phishing websites, which are nowadays in a considerable rise, have the same look as legitimate sites.

Medical diagnostic process optimization through the semantic integration of data resources.

In this paper we study the optimization of medical diagnostic process from the data access point of view. According to many studies which showed that optimized diagnostic process can considerably improve efficiency in health care industry, we present a new approach to data integration within a diagnostic process. It is our belief that a unified access to data resources throughout the whole diagnostic process considerably improves the efficiency of the process itself.

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.

Improving the reliability of medical software by predicting the dangerous software modules.

Software reliability analysis is inevitable for modern medical systems, since a large amount of medical system functionality is now dependent on software, and software does contribute to system failures. Most software reliability models are based on software failure data collected from the project. This creates a problem for the designers since, during the early stage, software failure data are not available.

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.