Trusting AI made decisions in healthcare by making them explainable.

OBJECTIVES: In solving the trust issues surrounding machine learning algorithms whose reasoning cannot be understood, advancements can be made toward the integration of machine learning algorithms into mHealth applications. The aim of this paper is to provide a transparency layer to black-box machine learning algorithms and empower mHealth applications to maximize their efficiency. METHODS: Using a machine learning testing framework, we present the process of knowledge transfer between a white-box model and a black-box model and the evaluation process to validate the success of the knowledge transfer. RESULTS: The presentation layer of the final output of the base white-box model and the knowledge-infused white-box model shows clear differences in reasoning. The correlation between the base black-box model and the new knowledge-infused model is very high, indicating that the knowledge transfer was successful. CONCLUSION: There is a clear need for transparency in digital health and health in general. Adding solutions to the toolbox of explainable artificial intelligence is one way to gradually decrease the obscurity of black-box models.

Trends in Physiotherapy of Chronic Low Back Pain Research: Knowledge Synthesis Based on Bibliometric Analysis.

BACKGROUND: Physiotherapy and chronic low back pain (CLBP) form a broad and quickly developing research area. The aim of this article was to holistically, thematically and chronologically analyze and synthesize the literature production in this research area and identify the most prolific research entities and research themes. METHODS: This article quantitatively and qualitatively analyzed research literature production harvested from the Scopus bibliometric database, using a triangulation of bibliometric and thematic analysis. For this, Excel 2024, Bibliometrix Biblioshiny 4.1 and VOSviewer version 1.6.20 softwares were used. RESULTS: In the Scopus database, 2843 data sources were found, which were published between 1974 and 26 February 2024. The growth trend has been linearly positive since the beginning of publication, and after 2018 exponential growth began. A review of the most prolific entities showed that the most literature was published in America, Europe and Australasia. The thematic analysis of the information sources identified six main themes (pathophysiology of CLBP and the quantification assessment tools, diagnostics and CLBP treatment, CLBP questionnaires and surveys, quality of life, complementary methods in physiotherapy and psychosocioeconomic aspects), while the chronological analysis revealed three main areas of development: assessment tools, CLBP processing and study methodology. CONCLUSIONS: The results of this bibliometric study present a good starting point for further research, providing taxonomy and research landscapes as a holistic framework offering multidisciplinary knowledge about CLBP, while chronological analysis provides a basis for identifying prospective research trends. This article offers an interdisciplinary view of the current issue of public health. The results of this study provide a basis for the development of both the physiotherapy and epidemiological fields.

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.

Research Trends in Motivation and Weight Loss: A Bibliometric-Based Review.

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.

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 quality of digital health software: Should we be concerned?

The digitalization of healthcare fuelled by advances in technology and the increased prevalence of mobile smart devices and health-related internet of things can offer equitable access to expert-level healthcare globally. Growing demand for telemedicine, mobile health apps, and advanced data analytics have further established their role in a modern information society during the Covid-19 crisis. Digital health is, in essence, powered by software (DHSW), which has to operate in the specific digital health environment characteristics and is therefore highly and intrinsically complex and prone to software defects and faults. Given the lack of standardization regarding DHSW quality, we explored the available reviewed research on this crucial topic in this brief paper, using a synthetic thematic analysis approach. We assert that neither the volume, distribution nor scope of the DHSW quality research content is satisfactory, and significant research gaps exist. Based on the presented evidence, we can only conclude that we should be concerned and that the time to act is now to ensure that the unavoidable increase of usage and prevalence of DHSW will not - in the end - reduce the quality of care due to subpar software and software-based digital health systems.

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.

Age-Related Changes in Lipid and Glucose Levels Associated with Drug Use and Mortality: An Observational Study.

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.

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.

Vaccine confidence among parents: Large scale study in eighteen European countries.

BACKGROUND: Despite the fact that vaccines save 2-3 million lives worldwide every year, a percentage of children are not getting appropriately vaccinated, thus leading to disease outbreaks. One of the major reasons of low vaccine uptake in Europe is vaccine hesitancy, contributing to the recent measles outbreaks. Monitoring of vaccine hesitancy is valuable in early identification of vaccine concerns. METHODS: We performed an eighteen country European survey on parents' attitudes and behaviors regarding their children's immunization. Parents having at least one child 1-4 years old were mostly recruited by primary care paediatricians to reply to a web-based questionnaire. The questionnaire was developed by the European Academy of Paediatrics Research in Ambulatory Setting Network steering committee, based on similar surveys. An individual level hesitancy score was constructed using the answers to 21 questions, and correlations of the score with socio-demographic characteristics and types of providers were explored. To assess inter country differences, a country level self -reported confidence was defined. RESULTS: Fifty six percent and 24% of 5736 respondents defined themselves as "not at all hesitant", and "somewhat hesitant", respectively. Parents who consulted general practitioners were more hesitant than parents who consulted pediatricians (p < 0.05). Consultation with homeopathists was associated with the highest reported hesitancy (p < 0.05). Vaccine confidence was highest in Portugal and Cyprus, and lowest in Bulgaria and Poland. CONCLUSION: The majority of parents in Europe believe in the importance of childhood vaccination. However, significant lack of confidence was found in certain European countries, highlighting the need for continuous monitoring, awareness and response plans. The possible influence of different types of healthcare providers on parental decisions demonstrated for the first time in our survey, calls for further research. Monitoring and continuous medical education efforts aimed mostly at those professionals who might not be likely to recommend vaccination are suggested.

Serious Game-based Intervention for Children with Developmental Disabilities.

BACKGROUND: Children with developmental disabilities may need support with motor skills such as balance improvement, cognitive skills such as vocabulary learning, or social skills such as adequate interpretation of emotional expressions. Digital interactive games could support the standard treatments. We aimed to review clinical studies which investigated the application of serious games in children with developmental disabilities. METHODS: We searched MEDLINE and Scopus on 05 May 2019 limited to the English language. We included people between two and 24 years of age who were affected by neurodevelopmental disorders and who received digital serious game-based medical interventions such as any computer- based or video-based games. We considered any study design reporting primary data. We used title, abstract, and full-text of journal articles to build diagnostic groups, and we described some selected specific game applications. RESULTS: The majority of the 145 relevant studies reported on autism spectrum disorder (ASD), attention deficit hyperactivity disorder (ADHD), developmental coordination disorder (DCD), and disabilities affecting intellectual abilities (DAIA). 30 of the 145 studies reported a randomized design. We detailed six specific applications aimed at improving abilities in children with ASD, ADHD, cerebral palsy, and Down syndrome. We visualized the diagnostic groups by bibliographic mapping, and limited the text to the title and abstract of journal articles. CONCLUSION: We identified promising results regarding anxiety reduction, stress regulation, emotion recognition, and rehabilitation. Currently, there appears to be a lack of clinical evidence that children with neurodevelopmental disorders can benefit from the application of serious games.

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.