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. RESULTS: Of a total of 15 171 patients analysed, 39.29% showed at least one positive reaction. The highest prevalences were noted for metals (nickel[II]sulfate hexahydrate: 16.33%) and fragrances (fragrance mix I: 6.70%). The correlation analysis showed the strongest correlation between mercapto mix and 2-mercaptobenzothiazole (61.2%), fragrance Mix II and hydroxyisohexyl 3-cyclohexene carboxaldehyde (50.5%), and potassium dichromate and cobalt(II) chloride hexahydrate (33.3%). CONCLUSIONS: Sensitization prevalences to the most common haptens were described, and their strongest correlations on a national level quantified. A comparison with other European results confirms already known associations between allergens in terms of cross-reactivity.

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. However, their backend is designed to collect sensitive information that is inputted by the victim. Discovering and detecting phishing websites has recently also gained the machine learning community's attention, which has built the models and performed classifications of phishing websites. This paper presents two dataset variations that consist of 58,645 and 88,647 websites labeled as legitimate or phishing and allow the researchers to train their classification models, build phishing detection systems, and mining association rules.

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. When combining the optimized data access with an existing algorithmic optimization method an optimized process can be achieved that takes into account the quality of a diagnosis, the individual needs of each patient, the associated costs, and the utilization of personnel/equipment. To enable an efficient management of data, we developed a semantic web based system for the integration of data resources within a medical diagnostic process. Then we combined the unified data access with our existing diagnostic process optimization framework that uses machine learning techniques and evolutionary algorithms. The new defined diagnostic process framework is finally used in a case-study for optimizing the diagnosing of the mitral valve prolapse syndrome in a regional hospital department.

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. However, a valuable knowledge can be learned from the analysis of previous projects and applied to the new ones. This paper presents the approach that predicts the potentially dangerous software modules under development based on the analysis of the already finished modules using the machine-learning techniques. On the basis of the prediction given by our method software designers are able to devote more testing effort to the dangerous parts of the system, which results in a more reliable medical software system.

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.

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.