Iskanje po katalogu digitalne knjižnice

Opis: Background: Evolutionary game theory (EGT), originating from Darwinian competition studies, offers a powerful framework for understanding complex healthcare interactions where multiple stakeholders with conflicting interests evolve strategies over time. Unlike traditional game theory, EGT accounts for bounded rationality and strategic evolution through imitation and selection. Aims and objectives: In our study, we use Synthetic Knowledge Synthesis (SKS) that integrates descriptive bibliometrics and bibliometric mapping to systematically analyze the application of EGT in healthcare. The SKS aimed to identify prolific research topics, suitable publishing venues, and productive institutions/countries for collaboration and funding. Data was harvested from the Scopus bibliographic database, encompassing 539 publications from 2000 to June 2025, Results: Production dynamics is revealing an exponential growth in scholarly output since 2019, with peak productivity in 2024. Descriptive bibliometrics showed China as the most prolific country (376 publications), followed by the United States and the United Kingdom. Key institutions are predominantly Chinese, and top journals include PLoS One and Frontiers in Public Health. Funding is primarily from Chinese entities like the National Natural Science Foundation of China. Bibliometric mapping identified five key research themes: game theory in cancer research, evolution game-based simulation of supply management, evolutionary game theory in epidemics, evolutionary games in trustworthy connected public health, and evolutionary games in collaborative governance. Conclusions: Despite EGT’s utility, significant research gaps exist in methodological robustness, data availability, contextual modelling, and interdisciplinary translation. Future research should focus on integrating machine learning, longitudinal data, and explicit ethical frameworks to enhance EGT’s practical application in adaptive, patient-centred healthcare systems
Ključne besede: evolutionary games theory, healthcare, complex healthcare systems, synthetic knowledge synthesis, thematic analysis
Objavljeno v DKUM: 29.10.2025; Ogledov: 0; Prenosov: 4
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Opis: V diplomski nalogi smo primerjali različne življenjske cikle razvoja programske opreme (SDLC). Obravnavali smo tradicionalne modele (slapovni model, V-model, spiralni model) in agilne pristope (Scrum, Kanban, RAD). S pomočjo empirične raziskave med slovenskimi strokovnjaki ugotavljamo, da agilni metodi, kot sta zlasti Scrum in Kanban, prevladujeta zaradi prilagodljivosti in učinkovitosti predvsem v manjših in srednje velikih projektih. Naloga podaja tudi praktične smernice in priporočila glede izbire ustreznega SDLC glede na vrsto projekta, kompleksnost in velikost ekipe, s čimer se podjetjem olajša izbira optimalnega razvojnega pristopa.
Ključne besede: življenjski cikel programske opreme (SDLC), primerjava modelov, agilne metode
Objavljeno v DKUM: 01.10.2025; Ogledov: 0; Prenosov: 12
Celotno besedilo (643,03 KB)

Opis: Artificial intelligence (AI) has witnessed an exponential increase in use in various applications. Recently, the academic community started to research and inject new AI-based approaches to provide solutions to traditional software-engineering problems. However, a comprehensive and holistic understanding of the current status needs to be included. To close the above gap, synthetic knowledge synthesis was used to induce the research landscape of the contemporary research literature on the use of AI in software engineering. The synthesis resulted in 15 research categories and 5 themes—namely, natural language processing in software engineering, use of artificial intelligence in the management of the software development life cycle, use of machine learning in fault/defect prediction and effort estimation, employment of deep learning in intelligent software engineering and code management, and mining software repositories to improve software quality. The most productive country was China (n = 2042), followed by the United States (n = 1193), India (n = 934), Germany (n = 445), and Canada (n = 381). A high percentage (n = 47.4%) of papers were funded, showing the strong interest in this research topic. The convergence of AI and software engineering can significantly reduce the required resources, improve the quality, enhance the user experience, and improve the well-being of software developers.
Ključne besede: software engineering, artificial intelligence, machine learning, synthetic knowledge synthesis, AI
Objavljeno v DKUM: 30.07.2025; Ogledov: 0; Prenosov: 12
Celotno besedilo (1,39 MB)
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Opis: Background: Artificial intelligence and machine learning are playing crucial roles in digital transformation, aiming to improve the efficiency, effectiveness, equity, and responsiveness of primary health systems and their services. Method: Using synthetic knowledge synthesis and bibliometric and thematic analysis triangulation, we identified the most productive and prolific countries, institutions, funding sponsors, source titles, publications productivity trends, and principal research categories and themes. Results: The United States and the United Kingdom were the most productive countries; Plos One and BJM Open were the most prolific journals; and the National Institutes of Health, USA, and the National Natural Science Foundation of China were the most productive funding sponsors. The publication productivity trend is positive and exponential. The main themes are related to natural language processing in clinical decision-making, primary health care optimization focusing on early diagnosis and screening, improving health-based social determinants, and using chatbots to optimize communications with patients and between health professionals. Conclusions: The use of machine learning in primary health care aims to address the significant global burden of so-called “missed diagnostic opportunities” while minimizing possible adverse effects on patients.
Ključne besede: primary health care, machine learning, research landscape, synthetic knowledge synthesis
Objavljeno v DKUM: 24.07.2025; Ogledov: 0; Prenosov: 6
Celotno besedilo (887,18 KB)

Opis: The first publication on the use of artificial intelligence (AI) in pediatrics dates back to 1984. Since then, research on AI in pediatrics has become much more popular, and the number of publications has largely increased. Consequently, a need for a holistic research landscape enabling researchers and other interested parties to gain insights into the use of AI in pediatrics has arisen. To fill this gap, a novel methodology, synthetic knowledge synthesis (SKS), was applied. Using SKS, we identified the most prolific countries, institutions, source titles, funding agencies, and research themes and the most frequently used AI algorithms and their applications in pediatrics. The corpus was extracted from the Scopus (Elsevier, The Netherlands) bibliographic database and analyzed using VOSViewer, version 1.6.20. Done An exponential growth in the literature was observed in the last decade. The United States, China, and Canada were the most productive countries. Deep learning was the most used machine learning algorithm and classification, and natural language processing was the most popular AI approach. Pneumonia, epilepsy, and asthma were the most targeted pediatric diagnoses, and prediction and clinical decision making were the most frequent applications.
Ključne besede: pediatrics, artificial intelligence, synthetic knowledge synthesis, bibliometrics, machine learning
Objavljeno v DKUM: 01.07.2025; Ogledov: 0; Prenosov: 9
Celotno besedilo (1,71 MB)
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