Navigating the AI frontiers in cardiovascular research: a bibliometric exploration and topic modeling

Artificial intelligence (AI) has emerged as a promising field in cardiovascular disease (CVD) research, offering innovative approaches to enhance diagnosis, treatment, and patient outcomes. In this study, we conducted bibliometric analysis combined with topic modeling to provide a comprehensive overview of the AI research landscape in CVD. Our analysis included 23,846 studies from Web of Science and PubMed, capturing the latest advancements and trends in this rapidly evolving field. By employing LDA (Latent Dirichlet Allocation) we identified key research themes, trends, and collaborations within the AI-CVD domain.The findings revealed the exponential growth of AI-related research in CVD, underscoring its immense potential to revolutionize cardiovascular healthcare. The annual scientific publication of machine learning papers in CVD increases continuously and significantly since 2016, with an overall annual growth rate of 22.8%. Almost half (46.2%) of the growth happened in the last 5 years. USA, China, India, UK and Korea were the top five productive countries in number of publications. UK, Germany and Australia were the most collaborative countries with a multiple country publication (MCP) value of 42.8%, 40.3% and 40.0% respectively. We observed the emergence of twenty-two distinct research topics, including “stroke and robotic rehabilitation therapy,” “robotic-assisted cardiac surgery,” and “cardiac image analysis,” which persisted as major topics throughout the years. Other topics, such as “retinal image analysis and CVD” and “biomarker and wearable signal analyses,” have recently emerged as dominant areas of research in cardiovascular medicine.Convolutional neural network appears to be the most mentioned algorithm followed by LSTM (Long Short-Term Memory) and KNN (K-Nearest Neighbours). This indicates that the future direction of AI cardiovascular research is predominantly directing toward neural networks and image analysis.As AI continues to shape the landscape of CVD research, our study serves as a comprehensive guide for researchers, practitioners, and policymakers, providing valuable insights into the current state of AI in CVD research. This study offers a deep understanding of research trends and paves the way for future directions to maximiz the potential of AI to effectively combat cardiovascular diseases

Trends and Determinants of Contraceptive Use among Young Married Women (Age 15-24) Based on the 2000, 2005, and 2011 Ethiopian Demographic and Health Surveys: A Multivariate Decomposition Analysis

Accessing family planning can reduce a significant proportion of maternal, infant, and childhood deaths. In Ethiopia, use of modern contraceptive met hods is low but is increasing. This study aimed to analyze the levels, trends, and determinants of modern contraceptive use and changes over time among young married women in Ethiopia. The study used data from the three Demographic Health Surveys (DHS) conducted in Ethiopia, in 2000, 2005, and 2011. Non-pregnant, young married women age 15-24 were included in the final samples, for sample sizes of 2,157 in 2000, 1,904 in 2005, and 2,146 in 2011. The major statistical techniques used were logistic regression for analysis of determinants of current contraceptive use and logit-based decomposition analysis of factors contributing to the recent changes. STATA 12 was employed for data management and analyses. All calculations presented in this paper were weighted for the sampling probabilities and non-response. Complex sampling procedures were also considered during testing of statistical significance. Among young married women, modern contraceptive prevalence increased from 6% in 2000 to 16% in 2005 and to 36% in 2011. The study found that young women’s wealth status, age, religion, education, family size concordance with husbands, and fertility preference for spacing or limiting births were significantly associated with their use of modern contraception. The decomposition analysis indicated that about a third of the overall change in modern contraceptive use was due to difference in women’s characteristics. For this component, changes in women’s age, educational status, religion, family size concordance, and fertility preference were significantly associated with change in modern contraceptive use. Particularly, an increase in women’s attainment of primary and above education accounted for about a tenth of the change in modern contraceptive use over the study period. About two-thirds of the increase in contraceptive use was due to difference in coefficients. Change in contraceptive use behavior among the rural population and among Orthodox Christians and Protestants showed a significant contribution to the increase. Other things being constant, about a third of the increase in modern contraceptive use in the past decade was due to change in contraceptive use behavior among the rural population. Programmatic interventions targeting poor, younger (adolescent), illiterate, and Muslim women would help to maintain the increasing trend in contraceptive use among young women in Ethiopia

Civil servants' demand for social health insurance in Northwest Ethiopia

BACKGROUND: Absence of reliable health insurance schemes is a key challenge to meet the universal health coverage target of the Sustainable Development Goals (SDGs). Ethiopian health system is characterized by under financing, low protection mechanisms for the poor, and lack of mechanisms of risk pooling and cost sharing. Ethiopia is implementing social health insurance (SHI) scheme to reduce out of pocket payment (OOP) and improve access and use of healthcare. This study aimed to determine the demand for SHI among civil servants and associated factors in Northwest Ethiopia. METHODS: An institution-based cross-sectional study was conducted in Bahir Dar city from 557 randomly selected civil servants using structured and self-administered questionaire. The questionnaire included questions measuring demand for SHI and demographic, socio-economic, healthcare related and personal and behavioral factors. Data were first entered in Epi-Info version 7.0 and transferred to SPSS version 20 for analysis. Descriptive statistics, bivariate and multivariable logistic regression analysis were performed. RESULTS: From the total calculated sample size of 557, 488 respondents returned the questionnaire giving a response rate of 88%. Nearly three-fourth of the respondents, 355 (72.7%), reported their need to be enrolled in a SHI scheme. Two-third of the respondents 325 (66.6%) were willing to pay for their enrollment. Overall, three hundred and two (61.9%) were demanding SHI. Having good awareness about health insurance [AOR = 4.39, 95% CI = (1.82–12.89)] and trust on a health insurance agency [AOR = 3.0, 95% CI = (1.57–5.72)], were significantly associated with the demand for SHI among civil servants. CONCLUSION: The demand for SHI among civil servants were higher. The awareness towards SHI and trust on the SHI agency were significantly associated with demand for SHI. As Ethiopia aspires to insure all employees of the formal sector, and improving the awareness of civil servants about SHI and the agency providing the service could improve demand for SHI. Further research is important on healthcare organizational and professional readiness to handle the upcoming insurance driven quality health service need and health seeking behavioral change

Initiatives, Concepts, and Implementation Practices of FAIR (Findable, Accessible, Interoperable, and Reusable) Data Principles in Health Data Stewardship Practice: Protocol for a Scoping Review

Data stewardship is an essential driver of research and clinical practice. Data collection, storage, access, sharing, and analytics are dependent on the proper and consistent use of data management principles among the investigators. Since 2016, the FAIR (findable, accessible, interoperable, and reusable) guiding principles for research data management have been resonating in scientific communities. Enabling data to be findable, accessible, interoperable, and reusable is currently believed to strengthen data sharing, reduce duplicated efforts, and move toward harmonization of data from heterogeneous unconnected data silos. FAIR initiatives and implementation trends are rising in different facets of scientific domains. It is important to understand the concepts and implementation practices of the FAIR data principles as applied to human health data by studying the flourishing initiatives and implementation lessons relevant to improved health research, particularly for data sharing during the coronavirus pandemic

Evidence-based practice and its associated factors among medical laboratory professionals in West Amhara hospitals, Northwest Ethiopia

Healthcare and clinical laboratory practices are constantly evolving, leading a need for evidence-based practice (EBP) among all laboratory professionals. However, EBP among laboratory professionals has not yet been studied in Ethiopia.This study aimed to determine EBP and associated factors among medical laboratory professionals in West Amhara hospitals, Northwest Ethiopia.An analytic and descriptive cross-sectional study was conducted in West Amhara hospitals from February to March 2014. Data were collected using a pretested, structured self-administered questionnaire. Bivariate and multivariate logistic regressions were performed to identify factors associated with evidence-based laboratory practice. Odds ratios with 95% confidence interval (CI) were computed and the level of significance determined.A total of 169 respondents completed the questionnaire (response rate 95.4%), 40.8% (n = 69) of whom had a good level of EBP. Knowledge about EBP [adjusted odds ratio (AOR) = 2.22, 95% CI: 1.10-4.49], internet access (AOR = 2.43, 95% CI: 1.12-5.29), adoption EBP (AOR = 2.79, 95% CI: 1.41-5.52) and being single in marital status (AOR = 2.21, 95% CI: 1.08-4.51) were factors associated with EBP in multivariable logistic regression after controlling the effects of the confounding variables.EBP among laboratory professionals was found to be low. Having good knowledge towards EBP, organizational adoption of EBP, having internet access in their organization and being single in marital status were factors associated with EBP

Guidelines and Standard Frameworks for AI in Medicine: Protocol for a Systematic Literature Review

Background: Applications of artificial intelligence (AI) are pervasive in modern biomedical science. In fact, research results suggesting algorithms and AI models for different target diseases and conditions are continuously increasing. While this situation undoubtedly improves the outcome of AI models, health care providers are increasingly unsure which AI model to use due to multiple alternatives for a specific target and the “black box” nature of AI. Moreover, the fact that studies rarely use guidelines in developing and reporting AI models poses additional challenges in trusting and adapting models for practical implementation. Objective: This review protocol describes the planned steps and methods for a review of the synthesized evidence regarding the quality of available guidelines and frameworks to facilitate AI applications in medicine. Methods: We will commence a systematic literature search using medical subject headings terms for medicine, guidelines, and machine learning (ML). All available guidelines, standard frameworks, best practices, checklists, and recommendations will be included, irrespective of the study design. The search will be conducted on web-based repositories such as PubMed, Web of Science, and the EQUATOR (Enhancing the Quality and Transparency of Health Research) network. After removing duplicate results, a preliminary scan for titles will be done by 2 reviewers. After the first scan, the reviewers will rescan the selected literature for abstract review, and any incongruities about whether to include the article for full-text review or not will be resolved by the third and fourth reviewer based on the predefined criteria. A Google Scholar (Google LLC) search will also be performed to identify gray literature. The quality of identified guidelines will be evaluated using the Appraisal of Guidelines, Research, and Evaluation (AGREE II) tool. A descriptive summary and narrative synthesis will be carried out, and the details of critical appraisal and subgroup synthesis findings will be presented. Results: The results will be reported using the PRISMA (Preferred Reporting Items for Systematic Review and Meta-Analyses) reporting guidelines. Data analysis is currently underway, and we anticipate finalizing the review by November 2023. Conclusions: Guidelines and recommended frameworks for developing, reporting, and implementing AI studies have been developed by different experts to facilitate the reliable assessment of validity and consistent interpretation of ML models for medical applications. We postulate that a guideline supports the assessment of an ML model only if the quality and reliability of the guideline are high. Assessing the quality and aspects of available guidelines, recommendations, checklists, and frameworks—as will be done in the proposed review—will provide comprehensive insights into current gaps and help to formulate future research directions. International Registered Report Identifier (IRRID): DERR1-10.2196/4710

Initiatives, Concepts, and Implementation Practices of the Findable, Accessible, Interoperable, and Reusable Data Principles in Health Data Stewardship: Scoping Review

Background: Thorough data stewardship is a key enabler of comprehensive health research. Processes such as data collection, storage, access, sharing, and analytics require researchers to follow elaborate data management strategies properly and consistently. Studies have shown that findable, accessible, interoperable, and reusable (FAIR) data leads to improved data sharing in different scientific domains. Objective: This scoping review identifies and discusses concepts, approaches, implementation experiences, and lessons learned in FAIR initiatives in health research data. Methods: The Arksey and O’Malley stage-based methodological framework for scoping reviews was applied. PubMed, Web of Science, and Google Scholar were searched to access relevant publications. Articles written in English, published between 2014 and 2020, and addressing FAIR concepts or practices in the health domain were included. The 3 data sources were deduplicated using a reference management software. In total, 2 independent authors reviewed the eligibility of each article based on defined inclusion and exclusion criteria. A charting tool was used to extract information from the full-text papers. The results were reported using the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines. Results: A total of 2.18% (34/1561) of the screened articles were included in the final review. The authors reported FAIRification approaches, which include interpolation, inclusion of comprehensive data dictionaries, repository design, semantic interoperability, ontologies, data quality, linked data, and requirement gathering for FAIRification tools. Challenges and mitigation strategies associated with FAIRification, such as high setup costs, data politics, technical and administrative issues, privacy concerns, and difficulties encountered in sharing health data despite its sensitive nature were also reported. We found various workflows, tools, and infrastructures designed by different groups worldwide to facilitate the FAIRification of health research data. We also uncovered a wide range of problems and questions that researchers are trying to address by using the different workflows, tools, and infrastructures. Although the concept of FAIR data stewardship in the health research domain is relatively new, almost all continents have been reached by at least one network trying to achieve health data FAIRness. Documented outcomes of FAIRification efforts include peer-reviewed publications, improved data sharing, facilitated data reuse, return on investment, and new treatments. Successful FAIRification of data has informed the management and prognosis of various diseases such as cancer, cardiovascular diseases, and neurological diseases. Efforts to FAIRify data on a wider variety of diseases have been ongoing since the COVID-19 pandemic. Conclusions: This work summarises projects, tools, and workflows for the FAIRification of health research data. The comprehensive review shows that implementing the FAIR concept in health data stewardship carries the promise of improved research data management and transparency in the era of big data and open research publishing. International Registered Report Identifier (IRRID): RR2-10.2196/2250

Cesarean delivery among women who gave birth in Dessie town hospitals, Northeast Ethiopia.

BackgroundOne of the key strategies for reducing maternal and perinatal morbidities and mortalities is the provision of skilled intrapartum care. While cesarean section is an important emergency obstetric intervention for saving the lives of mothers and newborns, a study comparing the prevalence of cesarean delivery is not sufficiently available in Ethiopia. This study aimed at assessing the prevalence and associated factors of cesarean delivery among women who gave birth at hospitals in Dessie town, Northeast Ethiopia.MethodsA facility based cross-sectional study was conducted between July and October 2013. A total of 520 women who gave birth in four hospitals (public = 1, private = 3) were interviewed. Face-to-face interviews using a pre-tested and structured questionnaire were conducted for primary data collection. Additionally, patients' charts were reviewed to collect mothers' clinical data. Bivariate and multiple logistic regressions analyses were conducted. Odds ratios and 95% confidence intervals were computed and a P-value of less than 0.05 was taken to declare the level of significance.ResultsA total of 512 mothers were included in the final analysis (response rate = 98.4%), the prevalence of cesarean delivery was found to be 47.6% (95% CI: 44.3, 51.1), While 46 (18.2%) of the procedure conducted in public and 198 (76.1%) were in private hospitals. Partograph monitoring [AOR = 3.84 95%CI: 2.24, 6.59], oxytocin administration [AOR = 4. 80 95%CI: 2.87-8.02], previous cesarean delivery [AOR = 2. 86 95%CI: 1.64-5.01] and place of delivery being a private hospital [AOR = 6. 79 95%CI: 4.18-11.01)] were associated with cesarean delivery.ConclusionThe prevalence of cesarean delivery was found to be high, and was significantly higher in private hospitals than a public facility. There is a need to conduct cesarean delivery audits to appropriately utilize scarce resources. Further an in-depth exploration of the experiences of women with cesarean delivery is necessary

Regional States of Federal Democratic Republic of Ethiopia.

<p>Regional States of Federal Democratic Republic of Ethiopia.</p

Approaches and Criteria for Provenance in Biomedical Data Sets and Workflows: Protocol for a Scoping Review

BackgroundProvenance supports the understanding of data genesis, and it is a key factor to ensure the trustworthiness of digital objects containing (sensitive) scientific data. Provenance information contributes to a better understanding of scientific results and fosters collaboration on existing data as well as data sharing. This encompasses defining comprehensive concepts and standards for transparency and traceability, reproducibility, validity, and quality assurance during clinical and scientific data workflows and research. ObjectiveThe aim of this scoping review is to investigate existing evidence regarding approaches and criteria for provenance tracking as well as disclosing current knowledge gaps in the biomedical domain. This review covers modeling aspects as well as metadata frameworks for meaningful and usable provenance information during creation, collection, and processing of (sensitive) scientific biomedical data. This review also covers the examination of quality aspects of provenance criteria. MethodsThis scoping review will follow the methodological framework by Arksey and O'Malley. Relevant publications will be obtained by querying PubMed and Web of Science. All papers in English language will be included, published between January 1, 2006 and March 23, 2021. Data retrieval will be accompanied by manual search for grey literature. Potential publications will then be exported into a reference management software, and duplicates will be removed. Afterwards, the obtained set of papers will be transferred into a systematic review management tool. All publications will be screened, extracted, and analyzed: title and abstract screening will be carried out by 4 independent reviewers. Majority vote is required for consent to eligibility of papers based on the defined inclusion and exclusion criteria. Full-text reading will be performed independently by 2 reviewers and in the last step, key information will be extracted on a pretested template. If agreement cannot be reached, the conflict will be resolved by a domain expert. Charted data will be analyzed by categorizing and summarizing the individual data items based on the research questions. Tabular or graphical overviews will be given, if applicable. ResultsThe reporting follows the extension of the Preferred Reporting Items for Systematic reviews and Meta-Analyses statements for Scoping Reviews. Electronic database searches in PubMed and Web of Science resulted in 469 matches after deduplication. As of September 2021, the scoping review is in the full-text screening stage. The data extraction using the pretested charting template will follow the full-text screening stage. We expect the scoping review report to be completed by February 2022. ConclusionsInformation about the origin of healthcare data has a major impact on the quality and the reusability of scientific results as well as follow-up activities. This protocol outlines plans for a scoping review that will provide information about current approaches, challenges, or knowledge gaps with provenance tracking in biomedical sciences. International Registered Report Identifier (IRRID)DERR1-10.2196/3175