Factors of Competitive Advantage of Territory on the Regional Level

The main aim of the paper is to identify the meaning of factors which influence creation, identification and utilization of competitive advantage of territory on the regional level. Through the theoretical knowledge and its analogical using in the conditions of regions and through the results of own researches, we characterize the meaning of various factors on the regional competitiveness. The basic assumption of the paper is that the market is the key element which defines the real competitive advantage which has the strategic meaning for regional development. The paper identifies the key factors of competitiveness including the cooperation in the conditions of regions and brings new theoretical approach to the utilization of competitive advantage in t erritories.Competitive advantage; regions; factors

Data processing pipeline for cardiogenic shock prediction using machine learning

Introduction: Recent advances in machine learning provide new possibilities to process and analyse observational patient data to predict patient outcomes. In this paper, we introduce a data processing pipeline for cardiogenic shock (CS) prediction from the MIMIC III database of intensive cardiac care unit patients with acute coronary syndrome. The ability to identify high-risk patients could possibly allow taking pre-emptive measures and thus prevent the development of CS. Methods: We mainly focus on techniques for the imputation of missing data by generating a pipeline for imputation and comparing the performance of various multivariate imputation algorithms, including k-nearest neighbours, two singular value decomposition (SVD)-based methods, and Multiple Imputation by Chained Equations. After imputation, we select the final subjects and variables from the imputed dataset and showcase the performance of the gradient-boosted framework that uses a tree-based classifier for cardiogenic shock prediction. Results: We achieved good classification performance thanks to data cleaning and imputation (cross-validated mean area under the curve 0.805) without hyperparameter optimization. Conclusion: We believe our pre-processing pipeline would prove helpful also for other classification and regression experiments