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Lipoprotein(a) as a risk factor in a cohort of hospitalised cardiovascular patients : A retrospective clinical routine data analysis
David 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: 35; Downloads: 0
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3.
Research trends in motivation and weight loss : a bibliometric-based review
Uroš 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: 62; Downloads: 4
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4.
Age-related changes in lipid and glucose levels associated with drug use and mortality : an observational study
Rene Markovič, Vladimir Grubelnik, Helena Blažun Vošner, Peter Kokol, Matej Završnik, Karmen Janša, Marjeta Zupet, Jernej Završnik, Marko Marhl, 2022, original scientific article

Abstract: Background: The pathogenesis of type 2 diabetes mellitus is complex and still unclear in some details. The main feature of diabetes mellitus is high serum glucose, and the question arises of whether there are other statistically observable dysregulations in laboratory measurements before the state of hyperglycemia becomes severe. In the present study, we aim to examine glucose and lipid profiles in the context of age, sex, medication use, and mortality. Methods: We conducted an observational study by analyzing laboratory data from 506,083 anonymized laboratory tests from 63,606 different patients performed by a regional laboratory in Slovenia between 2008 and 2019. Laboratory data-based results were evaluated in the context of medication use and mortality. The medication use database contains anonymized records of 1,632,441 patients from 2013 to 2018, and mortality data were obtained for the entire Slovenian population. Results: We show that the highest percentage of the population with elevated glucose levels occurs approximately 20 years later than the highest percentage with lipid dysregulation. Remarkably, two distinct inflection points were observed in these laboratory results. The first inflection point occurs at ages 55 to 59 years, corresponding to the greatest increase in medication use, and the second coincides with the sharp increase in mortality at ages 75 to 79 years. Conclusions: Our results suggest that medications and mortality are important factors affecting population statistics and must be considered when studying metabolic disorders such as dyslipidemia and hyperglycemia using laboratory data.
Keywords: diabetes, metabolic syndrome, hematological data, aging
Published in DKUM: 21.05.2024; Views: 105; Downloads: 1
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Agile Machine Learning Model Development Using Data Canyons in Medicine : A Step towards Explainable Artificial Intelligence and Flexible Expert-Based Model Improvement
Bojan Žlahtič, Jernej Završnik, Helena Blažun Vošner, Peter Kokol, David Šuran, Tadej Završnik, 2023, original scientific article

Abstract: Over the past few decades, machine learning has emerged as a valuable tool in the field of medicine, driven by the accumulation of vast amounts of medical data and the imperative to harness this data for the betterment of humanity. However, many of the prevailing machine learning algorithms in use today are characterized as black-box models, lacking transparency in their decision-making processes and are often devoid of clear visualization capabilities. The transparency of these machine learning models impedes medical experts from effectively leveraging them due to the high-stakes nature of their decisions. Consequently, the need for explainable artificial intelligence (XAI) that aims to address the demand for transparency in the decision-making mechanisms of black-box algorithms has arisen. Alternatively, employing white-box algorithms can empower medical experts by allowing them to contribute their knowledge to the decision-making process and obtain a clear and transparent output. This approach offers an opportunity to personalize machine learning models through an agile process. A novel white-box machine learning algorithm known as Data canyons was employed as a transparent and robust foundation for the proposed solution. By providing medical experts with a web framework where their expertise is transferred to a machine learning model and enabling the utilization of this process in an agile manner, a symbiotic relationship is fostered between the domains of medical expertise and machine learning. The flexibility to manipulate the output machine learning model and visually validate it, even without expertise in machine learning, establishes a crucial link between these two expert domains.
Keywords: XAI, explainable artificial intelligence, data canyons, machine learning, transparency, agile development, white-box model
Published in DKUM: 14.03.2024; Views: 214; Downloads: 19
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7.
Osveščenost staršev o bronhitisu pri predšolskem otroku
Monika Klančnik, 2019, undergraduate thesis

Abstract: Bronhitis je zelo pogosto obolenje pri otrocih v predšolskem obdobju, zato smo v zaključnem delu ugotavljali osveščenost staršev o bronhitisu in ukrepih. Zanimalo nas je, če otroci, ki obiskujejo vrtec pogosteje obolevajo za tem obolenjem kot ostali, ki vrtca ne obiskujejo. Raziskovalna metodologija: Uporabili smo deskriptivno metodo dela ter kvantitativno metodologijo. Podatke za raziskovalni del smo pridobili s pomočjo anketnega vprašalnika, na vprašanje pa je odgovorilo 30 staršev predšolskih otrok. Rezultati: Starši poznajo obolenje bronhitis, vendar se je z njim srečala manj kot tretjina otrok anketiranih oseb. Pri vseh se je kot simptom pokazal težko dihanje in kašelj, kar je tudi sicer najpogostejši simptom. Ukrepe pri bronhitisu poznajo bolj slabo. Prav tako pa jih je le tretjina mnenja, da obiskovanje vrtca povečuje tveganost za obolevnost njihovega otroka. Diskusija in zaključek: Obolenje bronhitis je pogosto predvsem pri predšolskih otrocih in v hladnih mesecih. Z dobrimi preventivnimi ukrepi in znanjem, ki bi ga starši lahko dobili od zdravstvenih delavcev, bi lahko to obolenje preprečili in ga omejili. Veliko staršev išče informacije preko interneta, kjer vsi viri niso zanesljivi. Z dobrimi navodili zdravstvenih delavcev, vestnimi starši, ki le te ukrepe izpolnjujejo, ter dobrimi vzgojitelji, ki opozorijo na obolenje, lahko zmanjšamo razširjenost RSV virusa in posledično bronhitisa.
Keywords: vrtčevski otroci, okužba zgornjih dihal, bronhitis, starši, medicinska sestra, pogosto obolenje.
Published in DKUM: 17.12.2019; Views: 1277; Downloads: 128
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8.
Pogostost nalezljivih bolezni otrok v vrtcu/skupinskem varstvu in doma
Erika Kolednik, 2019, undergraduate thesis

Abstract: Izhodišča: Otroške nalezljive bolezni so načeloma nenevarne in se otrok z njimi pravzaprav prekuži. V večini jih spremlja povišana telesna temperatura, ki je prvi opozorilni znak, da se z otrokom nekaj dogaja. Raziskovalne metode: Uporabljeni sta bili deskriptivna metoda dela in kvantitativna metodologija. Kot raziskovalni inštrument je bil uporabljen vprašalnik, ki je vseboval 12 vprašanj. V raziskavi je sodelovalo 212 staršev, katerih otroci obiskujejo vrtec, skupinsko varstvo ali so v domačem varstvu. Rezultati: V okviru raziskave je 75,00 % staršev, ki imajo svoje otroke v vrtcu, 6,13 % staršev, kjer so otroci v skupinskem varstvu in 18,87 % staršev, ki imajo otroke v domačem varstvu. Na podlagi raziskave smo ugotovili, da otroci, ki obiskujejo vrtec zbolevajo pogosteje za nalezljivimi boleznimi, kot tisti, ki so doma. Diskusija in zaključek: Otroci, ki so v vrtcu/skupinskem varstvu so bolj izpostavljeni različnim virusom in tako posledično hitreje in pogosteje zbolevajo. Vendar z starostjo bolezni upadajo. Otrok v domači oskrbi nima tolikšnega stika s drugimi otoki in tako tudi redkeje zboleva.
Keywords: predšolski otrok, higiena, virusi, respiratorne bolezni, bolezni z izpuščajem
Published in DKUM: 01.10.2019; Views: 966; Downloads: 136
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9.
Sleeping beauties in pediatrics
Jernej Završnik, Peter Kokol, 2016, other scientific articles

Abstract: Sleeping beauties (SBs) in science have been known for few decades; however, it seems that only recently have they become popular. An SB is a publication that ‘‘sleeps’’ for a long time and then almost suddenly awakes and becomes highly cited. SBs present interesting findings in science. Pediatrics research literature has not yet been analyzed for their presence, and 5 pediatrics SBs were discovered in this research. Their prevalence was approximately 0.011%. Some environments or periods are more ‘‘SB fertile’’ than others: 3 of 5 SBs were published in the journal Pediatrics, 4 originated from the United States, and 4 were published in the period from 1992 to 1993. No institutions or authors published more than 1 SB.
Keywords: pediatrics, paediatrics, scientific literature, citation, sleeping beauties, bilbliometrics, citation analysis, publications, research
Published in DKUM: 07.08.2017; Views: 1332; Downloads: 385
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10.
Outsourcing medical data analyses : can technology overcome legal, privacy and confidentiality issues?
Boštjan Brumen, Marjan Heričko, Andrej Sevčnikar, Jernej Završnik, Marko Hölbl, 2013, original scientific article

Abstract: Background: Medical data are gold mines for deriving the knowledge that could change the course of a single patient’s life or even the health of the entire population. A data analyst needs to have full access to relevant data, but full access may be denied by privacy and confidentiality of medical data legal regulations, especially when the data analyst is not affiliated with the data owner. Objective: Our first objective was to analyze the privacy and confidentiality issues and the associated regulations pertaining to medical data, and to identify technologies to properly address these issues. Our second objective was to develop a procedure to protect medical data in such a way that the outsourced analyst would be capable of doing analyses on protected data and the results would be comparable, if not the same, as if they had been done on the original data. Specifically, our hypothesis was there would not be a difference between the outsourced decision trees built on encrypted data and the ones built on original data. Methods: Using formal definitions, we developed an algorithm to protect medical data for outsourced analyses. The algorithm was applied to publicly available datasets (N=30) from the medical and life sciences fields. The analyses were performed on the original and the protected datasets and the results of the analyses were compared. Bootstrapped paired t tests for 2 dependent samples were used to test whether the mean differences in size, number of leaves, and the accuracy of the original and the encrypted decision trees were significantly different. Results: The decision trees built on encrypted data were virtually the same as those built on original data. Out of 30 datasets, 100% of the trees had identical accuracy. The size of a tree and the number of leaves was different only once (1/30, 3%, P=.19). Conclusions: The proposed algorithm encrypts a file with plain text medical data into an encrypted file with the data protected in such a way that external data analyses are still possible. The results show that the results of analyses on original and on protected data are identical or comparably similar. The approach addresses the privacy and confidentiality issues that arise with medical data and is adherent to strict legal rules in the United States and Europe regarding the processing of the medical data.
Keywords: medical data, disclosure control, medical confidentiality, data analysis, data security
Published in DKUM: 02.08.2017; Views: 1290; Downloads: 181
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