Characterization of global research trends and prospects on sudden coronary death: A literature visualization analysis.

Background: Sudden coronary death is a major global public health issue that has a significant impact on both individuals and society. Nowadays, scholars are active in sudden coronary death all over the world. However, no relevant bibliometric studies have been published. Here, we aim to gain a better understanding the current state of research and to explore potential new research directions through bibliometric analysis. Methods: Articles and reviews on sudden coronary death from 2012 to 2023 were retrieved from the Web of Science Core Collection (WoSCC). The topic search was conducted using the following keywords: ((("sudden cardiac death" OR "sudden death") AND (coronary OR "myocardial infarction")) OR "sudden coronary death"). Knowledge maps of authors, countries, institutions, journals, keywords, and citations were conducted by CiteSpace. Publication dynamics, hotspots, and frontiers were analyzed independently by authors. Results: A total of 2914 articles were identified from January 1, 2012 to June 20, 2023. The USA (n = 972) contributed the greatest absolute productivity and UK (centrality = 0.13) built a robust global collaboration. Harvard University was the institution with the highest number of publications (n = 143). Huikuri HV and Junttila MJ were the most published authors who devoted to searching for biomarkers of sudden coronary death. American Journal of Cardiology was the journal with the most publications, and Circulation was the most cited journal. Left ventricular ejection fraction, society, inflammation, and fractional flow reserve became novel burst words that lasted until 2023. Research on etiology and pathology, role of early risk factors in risk stratification, potential predictive biomarkers and novel measurement methods for the prevention and management of sudden coronary death were identified as the research hotspots and frontiers. Conclusion: Our knowledge and understanding of sudden coronary death have significantly improved. Ongoing efforts should focus on the various etiologies and pathologies of sudden coronary death. Furthermore, a novel sudden coronary death risk model, large-scale population studies, and the rational use of multiple indicators to individualize the assessment of sudden coronary death and other risk factors are other emerging research trends.

[Development and application of visual analysis teaching software for acupoint compatibility laws].

A user-friendly teaching software for visual analysis of acupoint compatibility laws has been developed based on the principles of partial order mathematics. This software is designed to provide auxiliary teaching of structured organization and visualization of law knowledge of compatibility data of acupuncture and moxibustion prescriptions from ancient texts, textbooks, and clinical case records. The software is installed as a plugin in the Microsoft Office Excel, allowing the generation of visually appealing graphs and associated rules that align with the cognitive patterns of teachers and students majoring in acupuncture and moxibustion. Its aim is to facilitate the discovery and analysis of underlying patterns and structured knowledge embedded in acupoint compatibility data, thus contributing to the enhancement of teaching effectiveness in acupoint compatibility.

Global trends and frontiers in research on exercise training for heart failure: a bibliometric analysis from 2002 to 2022.

Background: Heart failure is a common cardiovascular disease that imposes a heavy clinical and economic burden worldwide. Previous research and guidelines have supported exercise training as a safe, effective, and cost-efficient treatment to intervene in heart failure. The aim of this study was to analyze the global published literature in the field of exercise training for heart failure from 2002 to 2022, and to identify hot spots and frontiers within this research field. Methods: Bibliometric information on literature on the topic of exercise training for heart failure published between 2002 and 2022 was searched and collected in the Web of Science Core Collection. CiteSpace 6.1.R6 (Basic) and VOSviewer (1.6.18) were applied to perform bibliometric and knowledge mapping visualization analyses. Results: A total of 2017 documents were retrieved, with an upward-stable trend in the field of exercise training for heart failure. The US authors were in the first place with 667 documents (33.07%), followed by Brazilian authors (248, 12.30%) and Italian authors (182, 9.02%). The Universidade de São Paulo in Brazil was the institution with the highest number of publications (130, 6.45%). The top 5 active authors were all from the USA, with Christopher Michael O'Connor and William Erle Kraus publishing the most documents (51, 2.53%). The International Journal of Cardiology (83, 4.12%) and the Journal of Applied Physiology (78, 3.87%) were the two most popular journals, while Cardiac Cardiovascular Systems (983, 48.74%) and Physiology (299, 14.82%) were the two most popular categories. Based on the results of keyword co-occurrence network and co-cited reference network, the hot spots and frontiers of research in the field of exercise training for heart failure were high-intensity interval training, behaviour therapy, heart failure with preserved ejection fraction, and systematic reviews. Conclusion: The field of exercise training for heart failure has experienced two decades of steady and rapid development, and the findings of this bibliometric analysis provide ideas and references for relevant stakeholders such as subsequent researchers for further exploration.

Choosing optimal means of knowledge visualization based on eye tracking for online education.

In online education, the appropriate choice of means of knowledge visualization can reduce cognitive load and improve cognitive efficiency. However, no universal basis for selection can cause confusion in the pedagogical context. This study used the revised Bloom's taxonomy to combine the types of knowledge with cognitive goals. We used a course on marketing research as an example to summarize the choices for visualizing factual knowledge (FK), conceptual knowledge (CK), procedural knowledge (PK), and metacognitive knowledge (MK) through four experiments. Visualized cognitive stages were used to determine the cognitive efficiencies of visualization for different knowledge types. In this stage, eye tracking is used for collecting eye movement indicators to measure cognitive load. The cognitive goals stage is used to get cognitive goals of the means of knowledge visualization. Combining the two stages, we get the conclusions as follows: Teachers and students can mostly benefit from presenting FK and CK points via mind maps. Using mind maps to teach FK online could be indirectly beneficial for improving students' creativity. Concept maps may be chosen for this point if the linked knowledge points are PK and the achievement of the analytical objective is emphasized in the student's knowledge points. The flowchart can be used to display PK, while timelines could be utilized if the PK point is to be presented in a temporal dimension. Teachers should choose the curve area chart to display MK. A pie chart might be chosen and added more instructions. The findings suggest that mind maps are very effective as a means of knowledge visualization in online education. In the meantime, it suggests that overly simplistic graphs increase cognitive load, while it also raises the possibility that redundant information in the text may increase cognitive load.

Big knowledge visualization of the COVID-19 CIDO ontology evolution.

BACKGROUND: The extensive international research for medications and vaccines for the devastating COVID-19 pandemic requires a standard reference ontology. Among the current COVID-19 ontologies, the Coronavirus Infectious Disease Ontology (CIDO) is the largest one. Furthermore, it keeps growing very frequently. Researchers using CIDO as a reference ontology, need a quick update about the content added in a recent release to know how relevant the new concepts are to their research needs. Although CIDO is only a medium size ontology, it is still a large knowledge base posing a challenge for a user interested in obtaining the "big picture" of content changes between releases. Both a theoretical framework and a proper visualization are required to provide such a "big picture". METHODS: The child-of-based layout of the weighted aggregate partial-area taxonomy summarization network (WAT) provides a "big picture" convenient visualization of the content of an ontology. In this paper we address the "big picture" of content changes between two releases of an ontology. We introduce a new DIFF framework named Diff Weighted Aggregate Taxonomy (DWAT) to display the differences between the WATs of two releases of an ontology. We use a layered approach which consists first of a DWAT of major subjects in CIDO, and then drill down a major subject of interest in the top-level DWAT to obtain a DWAT of secondary subjects and even further refined layers. RESULTS: A visualization of the Diff Weighted Aggregate Taxonomy is demonstrated on the CIDO ontology. The evolution of CIDO between 2020 and 2022 is demonstrated in two perspectives. Drilling down for a DWAT of secondary subject networks is also demonstrated. We illustrate how the DWAT of CIDO provides insight into its evolution. CONCLUSIONS: The new Diff Weighted Aggregate Taxonomy enables a layered approach to view the "big picture" of the changes in the content between two releases of an ontology.

Development and application of visual analysis teaching software for acupoint compatibility laws / 中国针灸

A user-friendly teaching software for visual analysis of acupoint compatibility laws has been developed based on the principles of partial order mathematics. This software is designed to provide auxiliary teaching of structured organization and visualization of law knowledge of compatibility data of acupuncture and moxibustion prescriptions from ancient texts, textbooks, and clinical case records. The software is installed as a plugin in the Microsoft Office Excel, allowing the generation of visually appealing graphs and associated rules that align with the cognitive patterns of teachers and students majoring in acupuncture and moxibustion. Its aim is to facilitate the discovery and analysis of underlying patterns and structured knowledge embedded in acupoint compatibility data, thus contributing to the enhancement of teaching effectiveness in acupoint compatibility.

[Visualization and application method of acupuncture-moxibustion knowledge of senile dementia in ancient books based on partial order structure].

Acupuncture-moxibustion has affirmative curative effect in the prevention and treatment of senile dementia. Starting from the literature research, a visualization and application method of acupuncture-moxibustion knowledge of senile dementia in ancient books based on partial order structure is proposed. This method could extract and integrate the acupuncture-moxibustion knowledge of senile dementia contained in ancient books of traditional Chinese medicine, and establish a standardized, structured and visual knowledge graph. Applying this method to knowledge visual analysis and clinical auxiliary guidance could provide reference for combing the knowledge of ancient books of traditional Chinese medicine and transforming the knowledge of ancient books into clinical application.

Effect of color properties in multiple time series graph comprehension.

Multiple time series graphs are used prevalently in representing business and research data, but the use of color properties to visualize them to enhance comprehension is limited. This study explored the effect of hue and lightness in representing 4-time series data in relation to response time (RT) and accuracy. Two types of palettes were developed for each experiment: monochrome and multi-hue. The three sets of monochrome palettes created were red, green, and blue, while four equidistant hues in the color wheel were used in the multi-hue palette: red, blue, green, and purple. A total of forty people participated in the two experiments. Participants performed two tasks for both experiments: maximum and discrimination tasks. The monochrome experiment showed the primacy of green in terms of RT and accuracy in the discrimination task. RT and accuracy were significantly affected by lightness in the multi-hue experiment. For both tasks, RT was longer for 20% lightness and lowest at 60% lightness. Accuracy results were also consistent with RT. In the discrimination task, participants made more errors in 20% lightness and the highest accuracy for 60% and 80%.

BD2K Training Coordinating Center's ERuDIte: the Educational Resource Discovery Index for Data Science.

Data science is a field that has developed to enable efficient integration and analysis of increasingly large data sets in many domains. In particular, big data in genetics, neuroimaging, mobile health, and other subfields of biomedical science, promises new insights, but also poses challenges. To address these challenges, the National Institutes of Health launched the Big Data to Knowledge (BD2K) initiative, including a Training Coordinating Center (TCC) tasked with developing a resource for personalized data science training for biomedical researchers. The BD2K TCC web portal is powered by ERuDIte, the Educational Resource Discovery Index, which collects training resources for data science, including online courses, videos of tutorials and research talks, textbooks, and other web-based materials. While the availability of so many potential learning resources is exciting, they are highly heterogeneous in quality, difficulty, format, and topic, making the field intimidating to enter and difficult to navigate. Moreover, data science is rapidly evolving, so there is a constant influx of new materials and concepts. We leverage data science techniques to build ERuDIte itself, using data extraction, data integration, machine learning, information retrieval, and natural language processing to automatically collect, integrate, describe, and organize existing online resources for learning data science.

Exploration of research topics and visualization of knowledge domain of citation science on differentiated thyroid cancer / 中华内分泌外科杂志

Objective:To summarize the core research topics of the literature on differentiated thyroid cancer (DTC) , and to analyze the citation status to form the knowledge domain of citation science.Methods:Web of Science was used to search the literature on DTC, which was limited to April 04, 2021. The published records on DTC were identified. VOSviewer 1.6.11 and CiteSpace 5.5.R2 software were used to cluster and visualize the knowledge domain of citation.Results:A total of 8, 629 records on DTC were obtained, including 87, 973 citations, which showed that the publication volume increased year by year. Moreover, the trend of the annual records became more significant after the year of 2005. Meanwhile, it was reported that clinical staging and surgical management, as well as clinical researches on targeted therapy drugs, were treated as the currently hot research topics. The data showed that 115 records were cited more than 100 times, and 14 cited more than 300 times. And two records had been cited more than 1,000 times. Furthermore, the publication year of top15 were from 1988 to 2016, which also illustrated that the development of DTC researches were relatively slow before 2006. And the annual publication volume of DTC increased significantly with the publication of four highly cited records in 2006, even three with which have 1,000 citations. And the results revealed that the Consensus and Clinical Guidelines on DTC issued by ATA, as well as targeted therapy and radioactive 131I therapy on DTC had been highly cited. However, the newer highly cited documents on surgical treatment of DTC were still lacking. Conclusions:The current hot topics on DTC are focused on clinical staging, surgical management and targeted therapy. And a high-quality clinical guidelines and consensus and RCTs of targeted drugs on DTC have a significant impact on its development. Moreover, the further researches need to pay more attention to persistent/recurrent and metastatic DTC.

Knowledge Visualizations to Inform Decision Making for Improving Food Accessibility and Reducing Obesity Rates in the United States.

The aim of this article is to promote the use of knowledge visualization frameworks in the creation and transfer of complex public health knowledge. The accessibility to healthy food items is an example of complex public health knowledge. The United States Department of Agriculture Food Access Research Atlas (FARA) dataset contains 147 variables for 72,864 census tracts and includes 16 food accessibility variables with binary values (0 or 1). Using four-digit and 16-digit binary patterns, we have developed data analytical procedures to group the 72,684 U.S. census tracts into eight and forty groups respectively. This value-added FARA dataset facilitated the design and production of interactive knowledge visualizations that have a collective purpose of knowledge transfer and specific functions including new insights on food accessibility and obesity rates in the United States. The knowledge visualizations of the binary patterns could serve as an integrated explanation and prediction system to help answer why and what-if questions on food accessibility, nutritional inequality and nutrition therapy for diabetic care at varying geographic units. In conclusion, the approach of knowledge visualizations could inform coordinated multi-level decision making for improving food accessibility and reducing chronic diseases in locations defined by patterns of food access measures.

Visualizing an Ethics Framework: A Method to Create Interactive Knowledge Visualizations From Health Policy Documents.

BACKGROUND: Data have become an essential factor in driving health research and are key to the development of personalized and precision medicine. Primary and secondary use of personal data holds significant potential for research; however, it also introduces a new set of challenges around consent processes, privacy, and data sharing. Research institutions have issued ethical guidelines to address challenges and ensure responsible data processing and data sharing. However, ethical guidelines directed at researchers and medical professionals are often complex; require readers who are familiar with specific terminology; and can be hard to understand for people without sufficient background knowledge in legislation, research, and data processing practices. OBJECTIVE: This study aimed to visually represent an ethics framework to make its content more accessible to its stakeholders. More generally, we wanted to explore the potential of visualizing policy documents to combat and prevent research misconduct by improving the capacity of actors in health research to handle data responsibly. METHODS: We used a mixed methods approach based on knowledge visualization with 3 sequential steps: qualitative content analysis (open and axial coding, among others); visualizing the knowledge structure, which resulted from the previous step; and adding interactive functionality to access information using rapid prototyping. RESULTS: Through our iterative methodology, we developed a tool that allows users to explore an ethics framework for data sharing through an interactive visualization. Our results represent an approach that can make policy documents easier to understand and, therefore, more applicable in practice. CONCLUSIONS: Meaningful communication and understanding each other remain a challenge in various areas of health care and medicine. We contribute to advancing communication practices through the introduction of knowledge visualization to bioethics to offer a novel way to tackle this relevant issue.

Use of big data for drug development and for public and personal health and care.

The use of data analytics across the entire healthcare value chain, from drug discovery and development through epidemiology to informed clinical decision for patients or policy making for public health, has seen an explosion in the recent years. The increase in quantity and variety of data available together with the improvement of storing capabilities and analytical tools offer numerous possibilities to all stakeholders (manufacturers, regulators, payers, healthcare providers, decision makers, researchers) but most importantly, it has the potential to improve general health outcomes if we learn how to exploit it in the right way. This article looks at the different sources of data and the importance of unstructured data. It goes on to summarize current and potential future uses in drug discovery, development, and monitoring as well as in public and personal healthcare; including examples of good practice and recent developments. Finally, we discuss the main practical and ethical challenges to unravel the full potential of big data in healthcare and conclude that all stakeholders need to work together towards the common goal of making sense of the available data for the common good.

InterVisAR: An Interactive Visualization for Association Rule Search.

Association rule mining has been utilized extensively in many areas because it has the ability to discover relationships among variables in large databases. However, one main drawback of association rule mining is that it attempts to generate a large number of rules and does not guarantee that the rules are meaningful in the real world. Many visualization techniques have been proposed for association rules. These techniques were designed to provide a global overview of all rules so as to identify the most meaningful rules. However, using these visualization techniques to search for specific rules becomes challenging especially when the volume of rules is extremely large. In this study, we have developed an interactive association rule visualization technique, called InterVisAR, specifically designed for effective rule search. We conducted a user study with 24 participants, and the results demonstrated that InterVisAR provides an efficient and accurate visualization solution. We also verified that InterVisAR satisfies a non-factorial property that should be guaranteed in performing rule search. All participants also expressed high preference towards InterVisAR as it provides a more comfortable and pleasing visualization in association rule search.