Sepsis remains a significant contributor to neonatal mortality worldwide. However, the nonspecific nature of sepsis symptoms in neonates often leads to the necessity of empirical treatment, placing a burden of ineffective treatment on patients. Furthermore, the global challenge of antimicrobial resistance is exacerbating the situation. Artificial intelligence (AI) is transforming medical practice and in hospital settings. AI shows great potential for assessing sepsis risk and devising optimal treatment strategies. Background/Objectives: This review aims to investigate the application of AI in the detection and management of neonatal sepsis. Methods: A systematic literature review (SLR) evaluating AI methods in modeling and classifying sepsis between 1 January 2014, and 1 January 2024, was conducted. PubMed, Scopus, Cochrane, and Web of Science were systematically searched for English-language studies focusing on neonatal sepsis. Results: The analyzed studies predominantly utilized retrospective electronic medical record (EMR) data to develop, validate, and test AI models to predict sepsis occurrence and relevant parameters. Key predictors included low gestational age, low birth weight, high results of C-reactive protein and white blood cell counts, and tachycardia and respiratory failure. Machine learning models such as logistic regression, random forest, K-nearest neighbor (KNN), support vector machine (SVM), and XGBoost demonstrated effectiveness in this context. Conclusions: The summarized results of this review highlight the great promise of AI as a clinical decision support system for diagnostics, risk assessment, and personalized therapy selection in managing neonatal sepsis.

This article investigates the research online, purchase offline (ROPO) behaviour patterns of holiday packages buyers during the planning and buying process and identifies the socio-economic factors (gender, age, education, number of people, including minors, in a household, place of residence or financial status, etc.) determining its occurrence. The research purpose was achieved based on the research results covering the behaviour of the holiday packages buyers (N = 989). To analyse the factors determining the ROPO behaviour, the methods of multidimensional statistical analysis – classification and regression tree – were used and confirmed the existing correlation between age, educational level and residence of tourist services buyers and the occurrence of their ROPO behaviour. Interestingly, the conducted research did not show the correlation between economic factors and the occurrence of ROPO behaviour of holiday packages buyers.