1. Evolutionary game theory use in healthcare : a synthetic knowledge synthesisPeter Kokol, Jernej Završnik, Helena Blažun Vošner, Bojan Žlahtič, 2025, review article Abstract: 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
Keywords: evolutionary games theory, healthcare, complex healthcare systems, synthetic knowledge synthesis, thematic analysis
Published in DKUM: 29.10.2025; Views: 0; Downloads: 4
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2. The role of correspondence analysis in medical researchBojan Žlahtič, Peter Kokol, Helena Blažun Vošner, Jernej Završnik, 2024, other scientific articles Abstract: Correspondence analysis (CA) is a multivariate statistical and visualization technique. CA is extremely useful in analyzing either two- or multi-way contingency tables, representing some degree of correspondence between columns and rows. The CA results are visualized in easy-to-interpret “bi–plots,” where the proximity of items (values of categorical variables) represents the degree of association between presented items. In other words, items positioned near each other are more associated than those located farther away. Each bi-plot has two dimensions, named during the analysis. The naming of dimensions adds a qualitative aspect to the analysis. Correspondence analysis may support medical professionals in finding answers to many important questions related to health, wellbeing, quality of life, and similar topics in a simpler but more informal way than by using more complex statistical or machine learning approaches. In that way, it can be used for dimension reduction and data simplification, clustering, classification, feature selection, knowledge extraction, visualization of adverse effects, or pattern detection.
Keywords: public health, medical research, correspondence analysis, synthetic knowledge synthesis, exploratory data analysis, bibliometric
Published in DKUM: 30.07.2025; Views: 0; Downloads: 1
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3. Machine learning in primary health care : the research landscapeJernej Završnik, Peter Kokol, Bojan Žlahtič, Helena Blažun Vošner, 2025, review article Abstract: 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.
Keywords: primary health care, machine learning, research landscape, synthetic knowledge synthesis
Published in DKUM: 24.07.2025; Views: 0; Downloads: 6
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4. Artificial intelligence and pediatrics : synthetic knowledge synthesisJernej Završnik, Peter Kokol, Bojan Žlahtič, Helena Blažun Vošner, 2024, review article Abstract: 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.
Keywords: pediatrics, artificial intelligence, synthetic knowledge synthesis, bibliometrics, machine learning
Published in DKUM: 01.07.2025; Views: 0; Downloads: 8
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6. Zgodnji opozorilni kazalci povečanega tveganja za avtizem na preventivnih pregledih otrokNuša Zajšek, 2024, undergraduate thesis Abstract: Preventivni pregledi otrok so ključni za celovito zdravstveno oskrbo otrok, saj omogočajo temeljito oceno otrokovega zdravja ter zgodnje odkrivanje morebitnih težav. Medtem ko se ta obdobja hitrega razvoja in rasti odvijajo, se lahko pokažejo tudi zgodnji znaki avtizma. Namen zaključnega dela je s pregledom literature raziskati zgodnje opozorilne kazalce oziroma znake za avtizem, ki se lahko prepoznajo na preventivnih pregledih otrok do 6. leta starosti. Uporabili smo deskriptivno metodo dela. Iskanje literature smo izvedli s pomočjo iskalnih nizov v mednarodnih podatkovnih bazah: Cochrane Library, PubMed in Medline. Metoda vsebinske analize je bila uporabljena za sistematično razvrščanje relevantnih informacij iz literature. Potek iskanja literature smo vizualizirali s PRISMA diagramom. Od 250 zbranih člankov smo v analizo vključili 5 polno dostopnih vsebinsko primernih člankov. Ugotovljeno je bilo, da je zgodnji opozorilni znak avtizma na preventivnih pregledih največkrat zamuda v jezikovnem razvoju. Raziskave prav tako omenjajo, da lahko pri otrokovi starosti 6 mesecev pride do težav z razvojem fine motorike. Torej, avtizma ni mogoče diagnosticirati pred 12. mesecem starosti, zato je ključno natančno spremljanje razvoja otroka, še posebej v njegovem jezikovnem razvoju ter motoriki. Izboljšanje natančnosti diagnosticiranja in ozaveščenosti zdravstvenih delavcev ter družin ostajata pomemben izziv pri zgodnjem diagnosticiranju.
Keywords: zgodnje prepoznavanje, povečano tveganje, spremljanje razvoja, razvojna motnja, preventivni pregled
Published in DKUM: 23.10.2024; Views: 0; Downloads: 82
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8. The effects of diabetes and being overweight on patients with post-COVID-19 syndromeSimona Kirbiš, Nina Sobotkiewicz, Barbara Antolinc Schaubach, Jernej Završnik, Peter Kokol, Matej Završnik, Helena Blažun Vošner, 2023, original scientific article Keywords: post-COVID-19 syndrome, diabetes, overweight, COVID-19 pneumonia, observational study
Published in DKUM: 13.06.2024; Views: 124; Downloads: 26
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9. Lipoprotein(a) as a risk factor in a cohort of hospitalised cardiovascular patients : A retrospective clinical routine data analysisDavid Šuran, Tadej Završnik, Peter Kokol, Marko Kokol, Andreja Sinkovič, Franjo Naji, Jernej Završnik, Helena Blažun Vošner, Vojko Kanič, 2023, original scientific article Abstract: Lipoprotein(a) (Lp(a)) is a well-recognised risk factor for ischemic heart disease (IHD) and calcific aortic valve stenosis (AVS). Methods: A retrospective observational study of Lp(a) levels (mg/dL) in patients hospitalised for cardiovascular diseases (CVD) in our clinical routine was performed. The Lp(a)-associated risk of hospitalisation for IHD, AVS, and concomitant IHD/AVS versus other non-ischemic CVDs (oCVD group) was assessed by means of logistic regression. Results: In total of 11,767 adult patients, the association with Lp(a) was strongest in the IHD/AVS group (eβ = 1.010, p < 0.001), followed by the IHD (eβ = 1.008, p < 0.001) and AVS group (eβ = 1.004, p < 0.001). With increasing Lp(a) levels, the risk of IHD hospitalisation was higher compared with oCVD in women across all ages and in men aged ≤75 years. The risk of AVS hospitalisation was higher only in women aged ≤75 years (eβ = 1.010 in age < 60 years, eβ = 1.005 in age 60–75 years, p < 0.05). Conclusions: The Lp(a)-associated risk was highest for concomitant IHD/AVS hospitalisations. The differential impact of sex and age was most pronounced in the AVS group with an increased risk only in women aged ≤75 years.
Keywords: acute myocardial infarction, aortic valve stenosis, atherosclerosis, cardiovascular diseases, cardiovascular risk, ischemic heart disease, lipoprotein(a), postmenopausal women
Published in DKUM: 12.06.2024; Views: 137; Downloads: 13
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10. Research trends in motivation and weight loss : a bibliometric-based reviewUroš Železnik, Peter Kokol, Jasmina Starc, Danica Železnik, Jernej Završnik, Helena Blažun Vošner, 2023, review article Abstract: Obesity is a complex disease that, like COVID-19, has reached pandemic proportions. Consequently, it has become a rapidly growing scientific field, represented by an extensive body of research publications. Therefore, the aim of this study was to present the research trends in the scientific literature on motivation and weight loss. Because traditional knowledge synthesis approaches are not appropriate for analyzing large corpora of research evidence, we utilized a novel knowledge synthesis approach called synthetic knowledge synthesis (SKS) to generate new holistic insights into obesity research focusing on motivation. SKS is a triangulation of bibliometric analysis, bibliometric mapping, and content analysis. Using it, we analyzed the corpus of publications retrieved from the Scopus database, using the search string TITLE-ABS-KEY((obesity or overweight) and “weight loss” and motiv*) in titles, keywords, and abstracts, without any additional inclusion or exclusion criteria. The search resulted in a corpus of 2301 publications. The United States of America, the United Kingdom, and Australia were the most productive countries. Four themes emerged, namely, weight loss and weight-loss maintenance through motivational interventions, lifestyle changes supported by smart ICT, maintaining sustainable weight with a healthier lifestyle, and weight management on the level of primary healthcare and bariatric surgery. Further, we established that the volume of research literature is growing, as is the scope of the research. However, we observed a regional concentration of research and its funding in developed countries and almost nonexistent research cooperation between developed and less-developed countries.
Keywords: obesity, weight loss, motivation, synthetic knowledge synthesis, bibliometrics, content analysis
Published in DKUM: 05.06.2024; Views: 142; Downloads: 23
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