The role of correspondence analysis in medical research.

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.

The Effects of Diabetes and Being Overweight on Patients with Post-COVID-19 Syndrome.

In the aftermath of the COVID-19 pandemic, post-COVID-19 syndrome (PCS) remains a challenge and may continue to pose a major health problem in the future. Moreover, the influences of type 2 diabetes and being overweight on PCS remain unclear. This study aimed to assess these influences. We performed an observational study from October 2020 to July 2022, which included 466 patients (269 males and 197 females) with a median age of 65. They were hospitalized due to COVID-19 pneumonia and had persistent symptoms after 1 month of COVID-19 infection. The patients were divided into four groups according to the study objectives: patients with type 2 diabetes, overweight patients, overweight patients with type 2 diabetes, and average-weight patients without type 2 diabetes. The clinical and demographic data collected during hospitalization and regular visits to the Community Healthcare Center dr. Adolf Drolc Maribor were analyzed. Our results showed that type 2 diabetes patients had more difficult courses of treatment and longer hospitalizations. Moreover, more type 2 diabetes patients underwent rehabilitation than the other study groups. The prevailing symptoms of our patients with PCS were dyspnea and fatigue, mostly among female patients with type 2 diabetes. Our study also showed that more women with type 2 diabetes and overweight women with type 2 diabetes suffered from secondary infections. Furthermore, more overweight patients were treated in the intensive care unit than patients from the other groups. However, our study showed an interesting result: patients with type 2 diabetes had the shortest PCS durations. Type 2 diabetes and being overweight are risk factors for PCS onset and prolonged duration. Therefore, our data that revealed a shorter duration of PCS in type 2 diabetes patients than the other investigated groups was unexpected. We believe that answering the questions arising from our unexpected results will improve PCS treatment in general.

Profiling of patients with type 2 diabetes based on medication adherence data.

Introduction: Type 2 diabetes mellitus (T2DM) is a complex, chronic disease affecting multiple organs with varying symptoms and comorbidities. Profiling patients helps identify those with unfavorable disease progression, allowing for tailored therapy and addressing special needs. This study aims to uncover different T2DM profiles based on medication intake records and laboratory measurements, with a focus on how individuals with diabetes move through disease phases. Methods: We use medical records from databases of the last 20 years from the Department of Endocrinology and Diabetology of the University Medical Center in Maribor. Using the standard ATC medication classification system, we created a patient-specific drug profile, created using advanced natural language processing methods combined with data mining and hierarchical clustering. Results: Our results show a well-structured profile distribution characterizing different age groups of individuals with diabetes. Interestingly, only two main profiles characterize the early 40-50 age group, and the same is true for the last 80+ age group. One of these profiles includes individuals with diabetes with very low use of various medications, while the other profile includes individuals with diabetes with much higher use. The number in both groups is reciprocal. Conversely, the middle-aged groups are characterized by several distinct profiles with a wide range of medications that are associated with the distinct concomitant complications of T2DM. It is intuitive that the number of profiles increases in the later age groups, but it is not obvious why it is reduced later in the 80+ age group. In this context, further studies are needed to evaluate the contributions of a range of factors, such as drug development, drug adoption, and the impact of mortality associated with all T2DM-related diseases, which characterize these middle-aged groups, particularly those aged 55-75. Conclusion: Our approach aligns with existing studies and can be widely implemented without complex or expensive analyses. Treatment and drug use data are readily available in healthcare facilities worldwide, allowing for profiling insights into individuals with diabetes. Integrating data from other departments, such as cardiology and renal disease, may provide a more sophisticated understanding of T2DM patient profiles.

Lipoprotein(a) As a Risk Factor in a Cohort of Hospitalised Cardiovascular Patients: A Retrospective Clinical Routine Data Analysis.

INTRODUCTION: 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 (eß = 1.010, p < 0.001), followed by the IHD (eß = 1.008, p < 0.001) and AVS group (eß = 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 (eß = 1.010 in age < 60 years, eß = 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.

Lipoprotein(a) in Cardiovascular Diseases: Insight From a Bibliometric Study.

Lipoprotein(a) [Lp(a)] is a complex polymorphic lipoprotein comprised of a low-density lipoprotein particle with one molecule of apolipoprotein B100 and an additional apolipoprotein(a) connected through a disulfide bond. The serum concentration is mostly genetically determined and only modestly influenced by diet and other lifestyle modifications. In recent years it has garnered increasing attention due to its causal role in pre-mature atherosclerotic cardiovascular disease and calcific aortic valve stenosis, while novel effective therapeutic options are emerging [apolipoprotein(a) antisense oligonucleotides and ribonucleic acid interference therapy]. Bibliometric descriptive analysis and mapping of the research literature were made using Scopus built-in services. We focused on the distribution of documents, literature production dynamics, most prolific source titles, institutions, and countries. Additionally, we identified historical and influential papers using Reference Publication Year Spectrography (RPYS) and the CRExplorer software. An analysis of author keywords showed that Lp(a) was most intensively studied regarding inflammation, atherosclerosis, cardiovascular risk assessment, treatment options, and hormonal changes in post-menopausal women. The results provide a comprehensive view of the current Lp(a)-related literature with a specific interest in its role in calcific aortic valve stenosis and potential emerging pharmacological interventions. It will help the reader understand broader aspects of Lp(a) research and its translation into clinical practice.

Role of Agile in Digital Public Health Transformation.

The digitalisation of healthcare, fueled by advances in technology and the COVID-19 pandemic can not only empower equitable access to global expert-level healthcare but also make healthcare more patient-centric. Every digital health solution has one common fundamental component: they all run on computing platforms and are powered by complex software. Traditional software development life cycles have often failed in designing complex software; consequently, the agile approach was introduced. To assess the role of agile in digital public health transformation, we used the synthetic knowledge synthesis, a triangulation of bibliometric mapping, and thematic analysis to analyse the available literature harvested from PubMed. The analysis showed that the use of the agile approach is underutilised in developing digital health software. Moreover, the study revealed that health organisations did not yet embrace the agile culture and should adapt using innovative agile solutions to deliver clinical value to patients and public health systems. Following the software industry, where agile software development is becoming the mainstream approach also for sensitive and regulated software, it is becoming even more essential that the digital health software development process should be modernised. Furthermore, a shift to agile collaboration, agile decision-making, trial tolerance, active engagement, purposeful technology adoption, knowledge sharing, and an open agile innovation ecosystem must be achieved.

The Use of Machine Learning Techniques to Predict Diabetes in Patients with Cystic Fibrosis.

The accuracy of the prognosis of diabetes in patients with cystic fibrosis is crucial, as it highly connected with mortality and other complications. The prognosis of diabetes is a time-consuming process. Usually, it is performed by medical staff and can often lead to misdiagnosis. The aim of the study was to analyze and evaluate risk factors of developing diabetes in patients diagnosed with Cystic Fibrosis by using classification machine learning techniques. The ECFS data register was used to train and test the models. Visualization of our results using SHAP values highlights that most important features are age, antibiotic treatment, FEV1 value and lung transplant as risk predictors for diabetes.

Supporting Real World Decision Making in Coronary Diseases Using Machine Learning.

Cardiovascular diseases are one of the leading global causes of death. Following the positive experiences with machine learning in medicine we performed a study in which we assessed how machine learning can support decision making regarding coronary artery diseases. While a plethora of studies reported high accuracy rates of machine learning algorithms (MLA) in medical applications, the majority of the studies used the cleansed medical data bases without the presence of the "real world noise." Contrary, the aim of our study was to perform machine learning on the routinely collected Anonymous Cardiovascular Database (ACD), extracted directly from a hospital information system of the University Medical Centre Maribor). Many studies used tens of different machine learning approaches with substantially varying results regarding accuracy (ACU), hence they were not usable as a base to validate the results of our study. Thus, we decided, that our study will be performed in the 2 phases. During the first phase we trained the different MLAs on a comparable University of California Irvine UCI Heart Disease Dataset. The aim of this phase was first to define the "standard" ACU values and second to reduce the set of all MLAs to the most appropriate candidates to be used on the ACD, during the second phase. Seven MLAs were selected and the standard ACUs for the 2-class diagnosis were 0.85. Surprisingly, the same MLAs achieved the ACUs around 0.96 on the ACD. A general comparison of both databases revealed that different machine learning algorithms performance differ significantly. The accuracy on the ACD reached the highest levels using decision trees and neural networks while Liner regression and AdaBoost performed best in UCI database. This might indicate that decision trees based algorithms and neural networks are better in coping with real world not "noise free" clinical data and could successfully support decision making concerned with coronary diseasesmachine learning.

Application of bibliometrics in medicine: a historical bibliometrics analysis.

BACKGROUND: The application of bibliometrics in medicine enables one to analyse vast amounts of publications and their production patterns on macroscopic and microscopic levels. OBJECTIVES: The aim of the study was to analyse the historical perspective of research literature production regarding application of bibliometrics in medicine. METHODS: Publications related to application of bibliometrics in medicine from 1970 to 2018 were harvested from the Scopus bibliographic database. Reference Publication Year Spectroscopy was triangulated with the VOSViewer to identify historical roots and evolution of topics and clinical areas. RESULTS: The search resulted in 6557 publications. The literature production trend was positive. Historical roots analysis identified 33 historical roots and 16 clinical areas where bibliometrics was applied. DISCUSSION: The increase in productivity in application of bibliometrics in medicine might be attributed to increased use of quantitative metrics in research evaluation, publish or perish phenomenon and the increased use of evidence-based medicine. CONCLUSION: The trend of the literature production was positive. Medicine was in the forefront of knowledge development in bibliometrics. reference publication year spectroscopy proved to be an accurate method which was able to identify most of the historical roots.

Nursing Informatics: A Historical Bibliometric Analysis.

Computers were introduced into nursing care areas in the early 1950s; however, nursing informatics' historical roots emerged much earlier. Contrary to previous studies, which used manual review of the nursing informatics literature, we employed an automated, electronic approach with specialized software to identify its historical roots. The corpus of nursing informatics literature was harvested from Scopus using "informatic*" in information source titles, abstracts, and keywords, limited to the "subject area = nursing." The search resulted in 3805 publications containing 57 057 valid references. Fifteen historical sources were identified, the two oldest written by nursing pioneer Florence Nightingale. Other historical roots represent specific foundational and core scholarly works offering insight into the genesis and sustainment of particular bodies of literature in the field of informatics. Our study revealed that the field of nursing informatics has sought to respond to global health concerns and that through intensive development nursing informatics has become an independent research area affecting nursing advancements in general. Additionally, nursing informatics has influenced other disciplines. This study offered an in-depth look at nursing informatics literature, using a systematic approach to identify historical roots and analyze the evolution of topics and themes to gain more information about knowledge development in nursing informatics.

The impact of physical activity to the child's quality of life: a bibliometric study.

Background: The application of bibliometrics in healthcare research is becoming popular, however at present it is still an under-researched area. Methods: In our study we used a bibliometric technique called bibliometric mapping to visualize the published research regarding the influence of physical activity to children's quality of life. The research was visualized in the form of both chronological and cluster science landscapes. Science landscapes, contrary to conventional reviews, capture the relationships between multiple topics and concepts, enabling the generation of "synthetic reviews". Results: Evolutionarily, three distinct research phases appeared, namely research on influence of physical activity on various chronic non-communicable diseases; research on quality of life and childhood diseases related to physical activity; and outcome-related research. The research consists of six main topics: asthmatic child and exercising, blood diseases, health-related quality of life, obesity and chronic diseases, childhood obesity and behaviour, and depression and health outcomes. Conclusions: The study identified some research that may be helpful to general paediatricians whose everyday practice or research is not focused on physical activity and child's quality of life, but wants to learn about the taxonomy of the topics, the most interesting discoveries, guidelines and practices and the state of the art in the field. It also revealed some hidden association, otherwise not easily identified, even by informed researchers and clinicians.

How 'smart' is smart dentistry?

Background: Latest advances in information and health technologies enabled dentistry to follow the paradigm shift occurring in medicine - the transition to so called smart medicine. Consequently, the aim of this paper is to assess how 'smart' is smart dentistry as of the end of 2018. Methods: We analysed the state of the art in smart dentistry, performing bibliometric mapping on a corpus of smart dentistry papers found in the Scopus bibliographical database. Results: The search resulted in a corpus of 3451 papers, revealing that smart dentistry research is following the progress in smart medicine; however, there are some gaps in some specific areas like gamification and use of holistic smart dentistry systems. Conclusions: Smart dentistry is smart; however, it must become smarter.

Nursing Research Literature Production in Terms of the Scope of Country and Health Determinants: A Bibliometric Study.

PURPOSE: The aim of this paper was to present the results of the first study in which nursing research literature production was studied in the relation to country and health determinants. DESIGN: Bibliometric analysis was used. METHODS: The corpus of nursing publications was harvested from the Scopus indexing and abstracting database. Using research articles' metadata (funding acknowledgments, publication years, and author affiliations), we analyzed global trends in the nursing research literature production of funded and nonfunded publications. Next, we performed a regression analysis and correlation analysis relating nursing research productivity to health and country determinants. FINDINGS: The search resulted in 118,870 papers, among which 22.0% were funded (24.7% for G8 countries). Nursing literature production is exhibiting a positive trend. The United States is by far the most productive country in terms of funded and nonfunded literature production, although it is ranked only ninth in per capita production, for which Sweden is the most productive country regarding funded papers. The study also revealed that gross domestic product, human development factor, and gross national income were related to nursing research literature productivity. CONCLUSIONS: The positive trend in nursing research literature production (both funded and nonfunded) reveals a growth in nursing research funding. Regionally centered research literature production shows that the more developed and "rich" countries produce the majority of publications. A positive correlation is evident between country determinants and research literature production, as is a positive correlation between per capita literature research production and well-being and health determinants. CLINICAL RELEVANCE: Substantial growth in terms of nursing research literature production and research funding has been identified. While a limited amount of research in this area exists, this study revealed some interesting relations between nursing literature production and country and health determinants, which might motivate nursing researchers to pursue more intensive research and funders to support further growth of nursing research funding.

Historical, descriptive and exploratory analysis of application of bibliometrics in nursing research.

OBJECTIVE: The aim of the study was to analyse the literature production on application of bibliometrics in nursing research. METHODS: Historical, descriptive, and exploratory bibliometrics analyses were used. The papers were harvested from the Scopus bibliographical database (Elsevier, Maribor, Netherlands), on November 18th, 2018, using the search string bibliometric* in publication titles, abstracts, and keywords, limited to the subject of nursing, for the period 1970 to 2018. FINDINGS: The search resulted in 531 publications with 18 publications removed after manual inspection of publications abstracts. The literature production trend was positive. The nursing specific historical roots appeared only recently. The United States of America and Spain were the most productive countries, and most papers were published in Nutricion Hospitalaria and the Journal of Advanced Nursing. Descriptive analysis, research evaluation, content analysis, citation analyses, and trend analysis in nursing research were the most prolific themes. CONCLUSIONS: Applications of bibliometric in nursing is comparable to bibliometrics use in general, however there are some gaps including the use of altmetrics and of Big Data analysis.

Bibliometric analysis of the International Medical Informatics Association official journals.

Objectives: This research article aims to analyze the bibliometric characteristics of four official International Medical Informatics Association (IMIA) journals, namely: the International Journal of Medical Informatics, Methods of Information in Medicine, Applied Clinical Informatics, and Informatics for Health and Social Care.Method: We used descriptive bibliometrics to study the trends of literature production, identify documents` types, most prolific authors, institutions, countries, and most cited publications of all four IMIA journals. Additionally, we visualized the content of published publications using bibliometric mapping to identify journals' main themes and the most prolific and most cited research terms.Results: In total, 6,837 publications were published in all four IMIA journals. Among them, there were 5,137 original articles, meaning that articles were the leading document type. Research is being conducted globally among various research institutions. The most prolific countries are the United States of America, the United Kingdom, Germany, the Netherlands, and Canada. Thematic analyses of clusters show that themes are overlapping between all four journals.Conclusion: The journals contribute to the advances in technology related to health information systems, knowledge-based and decision-making systems, health literacy, and electronic health records.

Exploring an Unknown Territory: "Sleeping Beauties" in the Nursing Research Literature.

BACKGROUND: Sleeping Beauties (SBs) are publications that are scarcely cited in the years immediately following publication but then suddenly become highly cited later. Such publications have unique citation patterns and can reveal important developments in the field in which they appear. OBJECTIVES: No holistic analysis of nursing SBs has been done yet. The aim of this study was to identify and analyze the SB phenomenon in the nursing research literature. METHOD: The corpus for the nursing SB identification was harvested from the Web of Science Core Collection (Thomas Reuters) for the period 1934-2015. Citation histories of 212,239 publications were screened. From those, 3,209 publications with more than 100 citations were selected for analysis. We used our own software and applied the van Raan (2004) and Baumgartner (2010) criteria for SBs-a 5-year sleeping period with at most 10 citations during that time, an average of at least five citations per year after the first 10 years, with at least 100 citations in total. The knowledge context for SBs was determined using citing papers. All citing papers were analyzed with the help of VOSviewer software. RESULTS: Nine publications were identified as SBs (prevalence of 0.004%). The length of sleep duration ranged from 5 to 10 years (M = 6.8, SD = 2.0), depth of sleep ranged from 0.2 to 0.8 citations (M = 0.6, SD = 0.2), and awake intensity ranged from 6.4 to 15.0 citations (M = 11.0, SD = 3.8). The average number of citations to SBs was 229. Most nursing SBs were produced in the United States (n = 8) from top institutions in journals with high-impact factors. Nursing SBs covered topics including resilience, sampling in qualitative research, metasynthesis, postoperative pain in children, dementia rating scales, care of patients with Alzheimer's disease, nursing theory related to fatigue mechanisms in cancer patients, and family participation during resuscitation. Nursing SBs were cited by authors from a large number of institutions and countries; the number of publications citing nursing SBs is growing exponentially and showing increasing and global interest in the research presented in them. DISCUSSION: This study demonstrated that SBs in nursing are similar to other scientific disciplines. Existence of SBs suggests that nursing knowledge accumulation is supported by research and professional processes similar to those that emerged in other academic disciplines.

A bibliometric analysis of the Journal of Advanced Nursing, 1976-2015.

AIM: The aim of this study was to examine the publication characteristics and development of Journal of Advanced Nursing during its 40-year history. BACKGROUND: Bibliometric studies of single journals have been performed, but to the best of our knowledge, bibliometric analysis and bibliometric mapping have not yet been used to analyse the literature production of the Journal of Advanced Nursing. DESIGN/METHOD: Using descriptive bibliometrics, we studied the dynamics and trend patterns of literature production and identified document types and the most prolific authors, papers, institutions and countries. Bibliometric mapping was used to visualize the content of published articles and determine the most prolific research terms and themes published in Journal of Advanced Nursing and their evolution through time. We were also interested in determining whether there were any 'Sleeping Beauties' among the articles published in the journal. RESULTS: The study revealed a positive trend in literature production, although recently, the number of articles published in Journal of Advanced Nursing has slightly decreased. The most productive institutions are from the United Kingdom, which ranks in the highest place in terms of successful publishing in the journal. Thematic analysis showed that the most prolific themes corresponded to the basic aims and scope of the journal. CONCLUSION: Journal of Advanced Nursing contributes to advances in nursing research, practice and education as well as the quality of health care, teamwork and family care, with an emphasis on knowledge transfer and partnership between various healthcare professionals.