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The vast majority of medical problems are characterised by the relatively high spatial dimensionality of the task, which becomes problematic for many classic pattern recognition algorithms due to the well-known phenomenon of the curse of dimensionality. This creates the need to develop methods of space reduction, divided into strategies for the selection and extraction of features. The most commonly used tool of the second group is the PCA, which, unlike selection methods, does not select a subset of the original set of features and performs its mathematical transformation into a less dimensional form. However, natural downside of this algorithm is the fact that class context is not present in supervised learning tasks. This work proposes a feature extraction algorithm using the approach of the pca method, trying not only to reduce the feature space, but also trying to separate the class distributions in the available learning set. The problematic issue of the work was the creation of a method of feature extraction describing the prognosis for a chronic lymphocytic leukemia type B-CLL, which will be at least as good, or even better than when compared to other quality extractions. The purpose of the research was accomplished for binary and three-class cases in the event in which for verification of extraction quality, five algorithms of machine learning were applied. The obtained results were compared with the application of paired samples Wilcoxon test.

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The construction industry is an economic sector that is characterized by seasonality. Seasonal factors affect the volume of production, which in turn affects the accident rate. The aim of the research presented in the article was to develop a model for predicting the number of people injured in occupational accidents in the construction industry. Based on the analysis of statistical data and previous studies, the occurrence of certain regularities of the accidentality phenomenon was found, namely the long-term trend over many years, as well as seasonality and cyclicality over the course of a year. The found regularities were the basis for the assumptions that were made for the construction of the model. A mathematical model was built in the non-linear regression dimension. The model was validated by comparing the results of prediction errors generated by the developed model with the results of prediction errors generated by other known models, such as ARIMA, SARIMA, linear and polynomial models, which take into account the seasonality of the phenomenon. The constructed model enables the number of people injured in accidents in the construction industry in selected months of future years to be predicted with high accuracy. The obtained results can be the basis for making appropriate decisions regarding preventive and prophylactic measures in the construction industry. Commonly known mathematical tools available in the STATISTICA package were used to solve the given task.

Background The study aimed to assess the severity of symptoms of anxiety and depression in children with previously diagnosed psychiatric disorders during the COVID-19 pandemic in Poland. Methods Online questionnaires were used to investigate three groups of subjects: patients with a psychiatric diagnosis, primary school pupils, and children from children’s homes. A total of 167 children with their parents or guardians participated in the study. In addition to basic statistics, a multidimensional Centroid Class Principal Component Analysis (CCPCA) model was used. Results It was found that the strongest fear of the coronavirus was experienced by children from children’s homes, while the most severe depressive symptoms and state anxiety were observed among patients diagnosed with psychiatric disorders. Parental care by assisting with school education and lack of close contact with other people (less than two metres) at parents/guardians’ work had the most potent protective effect in reducing the fear of COVID-19. Conclusions There is a need for further research in children and adolescents to develop effective strategies for protecting their mental well-being when faced with social isolation or disease.

Background: The most important pathomechanism of Clostridioides difficile infections (CDI) is post-antibiotic intestinal dysbiosis. CDI affects both ambulatory and hospital patients. Aim: The objective of the study was to analyze the possibility of utilizing databases from the European Centre for Disease Prevention and Control subject to surveillance for the purpose of identifying areas that require intervention with respect to public health. Methods: The analysis encompassed data concerning CDI incidence and antibiotic consumption expressed as defined daily doses (DDD) and quality indicators for antimicrobial-consumption involving both ambulatory and hospital patients in 2016. Results: In 2016, in the European Union countries, total antibiotic consumption in hospital and outpatient treatment amounted to 20.4 DDD (SD 7.89, range 11.04-39.69); in ambulatory treatment using average of ten times more antibiotics than hospitals. In total, 44.9% of antibiotics used in outpatient procedures were broad-spectrum antibiotics. We have found a significant relationship between the quality of antibiotics and their consumption: The more broad-spectrum antibiotics prescribed, the higher the sales of antibiotics both in the community sector and in total. CDI incidence did not statistically significantly correlate with the remaining factors analyzed on a country-wide level. Conclusion: Antibiotic consumption and the CDI incidence may depend on many national variables associated with local systems of healthcare organization and financing. Their interpretation in international comparisons does not give clear-cut answers and requires caution.

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