Dataset Information

Tracking Knowledge Evolution in Cloud Health Care Research: Knowledge Map and Common Word Analysis.

ABSTRACT: BACKGROUND:With the continuous development of the internet and the explosive growth in data, big data technology has emerged. With its ongoing development and application, cloud computing technology provides better data storage and analysis. The development of cloud health care provides a more convenient and effective solution for health. Studying the evolution of knowledge and research hotspots in the field of cloud health care is increasingly important for medical informatics. Scholars in the medical informatics community need to understand the extent of the evolution of and possible trends in cloud health care research to inform their future research. OBJECTIVE:Drawing on the cloud health care literature, this study aimed to describe the development and evolution of research themes in cloud health care through a knowledge map and common word analysis. METHODS:A total of 2878 articles about cloud health care was retrieved from the Web of Science database. We used cybermetrics to analyze and visualize the keywords in these articles. We created a knowledge map to show the evolution of cloud health care research. We used co-word analysis to identify the hotspots and their evolution in cloud health care research. RESULTS:The evolution and development of cloud health care services are described. In 2007-2009 (Phase I), most scholars used cloud computing in the medical field mainly to reduce costs, and grid computing and cloud computing were the primary technologies. In 2010-2012 (Phase II), the security of cloud systems became of interest to scholars. In 2013-2015 (Phase III), medical informatization enabled big data for health services. In 2016-2017 (Phase IV), machine learning and mobile technologies were introduced to the medical field. CONCLUSIONS:Cloud health care research has been rapidly developing worldwide, and technologies used in cloud health research are simultaneously diverging and becoming smarter. Cloud-based mobile health, cloud-based smart health, and the security of cloud health data and systems are three possible trends in the future development of the cloud health care field.

SUBMITTER: Gu D

PROVIDER: S-EPMC7064966 | biostudies-literature | 2020 Feb

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Tracking Knowledge Evolution in Cloud Health Care Research: Knowledge Map and Common Word Analysis.

Gu Dongxiao D Yang Xuejie X Deng Shuyuan S Liang Changyong C Wang Xiaoyu X Wu Jiao J Guo Jingjing J

Journal of medical Internet research 20200225 2

<h4>Background</h4>With the continuous development of the internet and the explosive growth in data, big data technology has emerged. With its ongoing development and application, cloud computing technology provides better data storage and analysis. The development of cloud health care provides a more convenient and effective solution for health. Studying the evolution of knowledge and research hotspots in the field of cloud health care is increasingly important for medical informatics. Scholars ...[more]

PMID: 32130115

Similar Datasets

Project description:BackgroundThe recent surge in clinical and nonclinical health-related data has been accompanied by a concomitant increase in personal health data (PHD) research across multiple disciplines such as medicine, computer science, and management. There is now a need to synthesize the dynamic knowledge of PHD in various disciplines to spot potential research hotspots.ObjectiveThe aim of this study was to reveal the knowledge evolutionary trends in PHD and detect potential research hotspots using bibliometric analysis.MethodsWe collected 8281 articles published between 2009 and 2018 from the Web of Science database. The knowledge evolution analysis (KEA) framework was used to analyze the evolution of PHD research. The KEA framework is a bibliometric approach that is based on 3 knowledge networks: reference co-citation, keyword co-occurrence, and discipline co-occurrence.ResultsThe findings show that the focus of PHD research has evolved from medicine centric to technology centric to human centric since 2009. The most active PHD knowledge cluster is developing knowledge resources and allocating scarce resources. The field of computer science, especially the topic of artificial intelligence (AI), has been the focal point of recent empirical studies on PHD. Topics related to psychology and human factors (eg, attitude, satisfaction, education) are also receiving more attention.ConclusionsOur analysis shows that PHD research has the potential to provide value-based health care in the future. All stakeholders should be educated about AI technology to promote value generation through PHD. Moreover, technology developers and health care institutions should consider human factors to facilitate the effective adoption of PHD-related technology. These findings indicate opportunities for interdisciplinary cooperation in several PHD research areas: (1) AI applications for PHD; (2) regulatory issues and governance of PHD; (3) education of all stakeholders about AI technology; and (4) value-based health care including "allocative value," "technology value," and "personalized value."

Project description:BackgroundCloud computing is an innovative paradigm that provides users with on-demand access to a shared pool of configurable computing resources such as servers, storage, and applications. Researchers claim that information technology (IT) services delivered via the cloud computing paradigm (ie, cloud computing services) provide major benefits for health care. However, due to a mismatch between our conceptual understanding of cloud computing for health care and the actual phenomenon in practice, the meaningful use of it for the health care industry cannot always be ensured. Although some studies have tried to conceptualize cloud computing or interpret this phenomenon for health care settings, they have mainly relied on its interpretation in a common context or have been heavily based on a general understanding of traditional health IT artifacts, leading to an insufficient or unspecific conceptual understanding of cloud computing for health care.ObjectiveWe aim to generate insights into the concept of cloud computing for health IT research. We propose a taxonomy that can serve as a fundamental mechanism for organizing knowledge about cloud computing services in health care organizations to gain a deepened, specific understanding of cloud computing in health care. With the taxonomy, we focus on conceptualizing the relevant properties of cloud computing for service delivery to health care organizations and highlighting their specific meanings for health care.MethodsWe employed a 2-stage approach in developing a taxonomy of cloud computing services for health care organizations. We conducted a structured literature review and 24 semistructured expert interviews in stage 1, drawing on data from theory and practice. In stage 2, we applied a systematic approach and relied on data from stage 1 to develop and evaluate the taxonomy using 14 iterations.ResultsOur taxonomy is composed of 8 dimensions and 28 characteristics that are relevant for cloud computing services in health care organizations. By applying the taxonomy to classify existing cloud computing services identified from the literature and expert interviews, which also serves as a part of the taxonomy, we identified 7 specificities of cloud computing in health care. These specificities challenge what we have learned about cloud computing in general contexts or in traditional health IT from the previous literature. The summarized specificities suggest research opportunities and exemplary research questions for future health IT research on cloud computing.ConclusionsBy relying on perspectives from a taxonomy for cloud computing services for health care organizations, this study provides a solid conceptual cornerstone for cloud computing in health care. Moreover, the identified specificities of cloud computing and the related future research opportunities will serve as a valuable roadmap to facilitate more research into cloud computing in health care.

Project description:Presently, the focus of target detection is shifting towards the integration of information acquired from multiple sensors. When faced with a vast amount of data from various sensors, ensuring data security during transmission and storage in the cloud becomes a primary concern. Data files can be encrypted and stored in the cloud. When using data, the required data files can be returned through ciphertext retrieval, and then searchable encryption technology can be developed. However, the existing searchable encryption algorithms mainly ignore the data explosion problem in a cloud computing environment. The issue of authorised access under cloud computing has yet to be solved uniformly, resulting in a waste of computing power by data users when processing more and more data. Furthermore, to save computing resources, ECS (encrypted cloud storage) may only return a fragment of results in response to a search query, lacking a practical and universal verification mechanism. Therefore, this article proposes a lightweight, fine-grained searchable encryption scheme tailored to the cloud edge computing environment. We generate ciphertext and search trap gates for terminal devices based on bilinear pairs and introduce access policies to restrict ciphertext search permissions, which improves the efficiency of ciphertext generation and retrieval. This scheme allows for encryption and trapdoor calculation generation on auxiliary terminal devices, with complex calculations carried out on edge devices. The resulting method ensures secure data access, fast search in multi-sensor network tracking, and accelerates computing speed while maintaining data security. Ultimately, experimental comparisons and analyses demonstrate that the proposed method improves data retrieval efficiency by approximately 62%, reduces the storage overhead of the public key, ciphertext index, and verifiable searchable ciphertext by half, and effectively mitigates delays in data transmission and computation processes.

Project description:ObjectivesTo identify the cooperation and impact of authors, countries, institutions, and journals, evaluate the knowledge base, find the hotspot trends, and detect the emerging topics regarding ferroptosis research.MethodsThe articles and reviews related to ferroptosis were obtained from the Web of Science Core Collection on November 1, 2020. Two scientometric software (CiteSpace 5.7 and VOSviewer 1.6.15) were used to perform bibliometric and knowledge-map analysis.ResultsA total of 1,267 papers were included, in 466 academic journals by 6,867 authors in 438 institutions from 61 countries/regions. The ferroptosis-related publications were increasing rapidly. Cell Death & Disease published the most papers on ferroptosis, while Cell was the top co-cited journal, publication journals and co-cited journals were major in the molecular and biology fields. The United States and China were the most productive countries; meanwhile, the University of Pittsburgh, Columbia University and Guangzhou Medical University were the most active institutions. Brent R Stockwell published the most papers, while Scott J Dixon had the most co-citations; simultaneously, active cooperation existed in ferroptosis researchers. Ten references on reviews, mechanisms, and diseases were regarded as the knowledge base. Five main aspects of ferroptosis research included regulation mechanisms, nervous system injury, cancer, relationships with other types of cell death, and lipid peroxidation. The latest hotspots were nanoparticle, cancer therapy, iron metabolism, and in-depth mechanism. Notably Nrf2 might have turning significance. The emerging topics on ferroptosis research were the further molecular mechanism of ferroptosis and the wider application of ferroptosis-related disease with advanced technology.ConclusionThis study performed a full overview of the ferroptosis research using bibliometric and visual methods. The information would provide helpful references for scholars focusing on ferroptosis.

Project description:BackgroundOne of the requirements for scientists and researchers to enter any field of science is to have a comprehensive and accurate understanding of that discipline.ObjectiveThis study aims to draw a science map, provide structural analysis, explore the evolution, and determine new trends in research articles published in the field of breast cancer.MethodsThis study comprised a descriptive survey with a scientometric approach. Data were collected from MEDLINE using a search strategy based on Medical Subject Heading (MeSH) terms. This study used science mapping, which provides a visual representation and a longitudinal evolution of possible interrelations between scientific areas, documents, or authors, thus reflecting the cognitive architecture of science mapping. For this scientometric evaluation of the topic of breast cancer research, a very long period was considered for data collection. Moreover, due to the availability of numerous publications in the database, the assessment was divided into three different periods ranging from 1988 to 2020.ResultsA total of 12,577 records related to scientometric studies were extracted. The field of breast cancer research demonstrated three diagrams containing the most relevant themes for the three chronological periods evaluated. Each diagram was plotted based on the centrality and density linked to each research topic. The research output in the field was observed to revolve around 8 areas or themes: radiation injury, cardiovascular disease, fibroadenoma, antineoplastic agent, estrogen antagonistic, immunohistochemistry, soybean, and epitopes, each represented with different colors.ConclusionsIn the strategic diagrams, the themes were both well developed and important for the structuring of a research field. The first quadrant comprised motor themes of the specialty, which present strong centrality and high density (eg, corticosteroid antineoplastic age, stem cell, T-lymphocyte, protein tyrosine kinase, dietary, and phosphatidyl inositol-3-kinase). In the second quadrant of diagram, themes have well-developed internal ties but unimportant external ties, as they are of only marginal importance for the field. These themes are very specialized and peripheral (eg, DNA-binding). In the third quadrant, themes are both weakly developed and marginal. The themes in this quadrant have low density and centrality and mainly represent either emerging or declining themes (eg, ovarian neoplasm). Themes in the fourth quadrant of the strategic diagram are considered important for a research field but are not fully developed. This quadrant contains transversal and general, basic themes (eg, immunohistochemistry). Scientometric analysis of breast cancer research can be regarded as a roadmap for future research and policymaking for this important field.

Project description:BackgroundDespite the increasing mobilization of researchers and funding organizations around knowledge translation (KT) in Canada and elsewhere, many questions have been only partially answered, particularly in the field of population health. This article presents the results of a systematic process to draw out possible avenues of collaboration for researchers, practitioners and decision-makers who work in the area of KT. The main objective was to establish a research agenda on knowledge translation in population health.MethodsUsing the Concept Mapping approach, the research team wanted to identify priority themes for the development of research on KT in population health. Mapping is based on multivariate statistical analyses (multidimensional scaling and hierarchical cluster analysis) in which statements produced during a brainstorming session are grouped in weighted clusters. The final maps are a visual representation of the priority themes of research on KT. Especially designed for facilitating consensus in the understanding and organization of various concepts, the Concept Mapping method proved suitable for achieving this objective.ResultsThe maps were produced by 19 participants from university settings, and from institutions within the health and social services network. Three main perspectives emerge from this operation: (1) The evaluation of the effectiveness of KT efforts is one of the main research priorities; (2) The importance of taking into consideration user contexts in any KT effort; (3) The challenges related to sharing power for decision-making and action-taking among various stakeholder groups. These perspectives open up avenues of collaboration for stakeholders who are involved in research on KT. Besides these three main perspectives, the concept maps reveal three other trends which should be emphasized.ConclusionThe Concept Mapping process reported in this article aimed to provoke collective reflection on the research questions that should be studied, in order to foster coherence in research activities in the field of population health. Based on this, it is appropriate to continue to support the development of research projects in KT and the formation of research teams in this field. Research on KT must lead to concrete outcomes within communities that are interested in the question.

Dataset Information

Tracking Knowledge Evolution in Cloud Health Care Research: Knowledge Map and Common Word Analysis.

Publications

Tracking Knowledge Evolution in Cloud Health Care Research: Knowledge Map and Common Word Analysis.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets