The disease spectrum of Chinese residents is undergoing fundamental changes. Chronic diseases have become the most important public health problem.
To reveal the evolution logic of hot topics in the field of chronic disease management, the research hotspots and evolution paths of chronic disease management were visually analyzed. A prediction model based on trend degree is generated to reveal the future development direction of hot topics in the field of chronic disease management.
The literature search time was as of April 30, 2023. The type was limited to academic journals, and non-research literatures such as academic papers, meeting minutes, news reports, and expert consensus were excluded. Relevant literatures are retrieved in China National Knowledge Infrastructure (CNKI), VIP Chinese Science and Technology Journal Full-text Database, and Wanfang Data Knowledge Service Platform. In order to ensure the quality of the included literature, the journal types were limited to Peking University Core, China Science Citation Database (CSCD) and Chinese Social Sciences Citation Index (CSSCI). Based on text mining technology and bibliometric methods, this paper used VOSviewer software to draw a co-word timeline network map, and deeply analyzes the structural relationship and evolution characteristics of research hotspots in chronic disease management. Based on cluster analysis and strategic coordinate analysis, the research status and development trend of its clustering theme were revealed. The topic trend degree index was constructed by weighting and superimposing the characteristic indexes including topic novelty and intensity indexes. The three exponential smoothing way was used to predict and analyze the time series of topic trend degree.
At the initial stage, the research focus in the field of chronic disease management was policy-oriented and fragmented, and gradually developed towards a diversified trend, forming a multi-center network structure with core nodes, such as "diabetes" "hypertension" "community" "management mode" "hierarchical diagnosis and treatment" "medical consortium" "Internet medicine", and "sports medicine integration".
The related literature in the field of chronic disease management showed an exponential growth trend. It has become a key research topic in academia. Multiple chronic disease management, chronic disease management of specific groups, psychological status of patients with chronic diseases, intelligent medical care, chronic disease management of traditional Chinese medicine, and big health management are emerging hot topics in the field of chronic disease management at this stage. Grassroots chronic disease management is a marginal research content, and the research results are not yet mature. In the future, we should focus on the research on the health management model of chronic disease comorbidity, continue to explore the deep integration mechanism of big data, cloud computing, Internet of things and wearable devices with chronic disease management, innovate the new model of chronic disease management based on the integration of medical treatment and prevention, and create a new health management plan for the whole population and the whole life cycle.
General practice played a crucial role in primary health care and its development had become a key measure to meet the growing health needs of the population. Understanding the research hotspots and development trends in general practice can better guide its research.
To analyze the research hotspots and development trends in general practice over the last five years, providing references for the development of general practice as a discipline.
The Web of Science core collection database was used as the data source to search for relevant literature in the field of general practice from January 2019 to November 2023, and co-occurrence analysis of authors, countries and institutions, co-cited literature cluster analysis, keyword cluster analysis and emergence analysis were performed using CiteSpace software.
A total of 9 580 relevant documents were retrieved. Research on general practice was mainly concentrated in the United States, the United Kingdom and Australia. The University of Melbourne had the most publications, and PARKER MAGIN was the author with the most publications. Current hotspots in general practice research included postgraduate education, hospice care, professional burnout among family physicians, telemedicine, qualitative research, with emphasis on demographics like females and the elderly, psychological well-being, and chronic disease management. The trends in this field were medical education and training, population health, and telemedicine.
Over the past five years, research hotspots in general practice have primarily focused on chronic disease management and treatment, postgraduate education in general practice, hospice care, occupational burnout among family physicians, key populations, and mental health. Furthermore, emerging trends indicate that telemedicine, population health, and general practice education and training will likely dominate future research directions in this field.
Responding to the increasing demand for privacy encryption in image-based medical big data, it is of great importance of proposing an innovative framework of coded-based privacy-preserving segmentation technology, and exploring the implementation pathways to facilitate the practical application of this technology from a collaborative perspective of technology and policy legislation.
To develop a privacy protection technology framework tailored for image-based medical big data, and propose policy and legislative coordination strategies to advance the technology's adoption, in order to enhance the healthcare informatization service system by combining technological innovation with policy support.
Construct the innovative framework for privacy preserving segmentation technology in medical image big data by literature review, theoretical analysis, technology framework development, experimental validation, and policy analysis, and then propose the policy and legislative coordination strategies.
We successfully construct the innovative framework for privacy preserving segmentation technology in medical image big data and though the effectiveness verification, and propose specific policy and legislative recommendations addressing the inadequacies of existing laws and regulations in areas such as cloud data processing, liability attribution, technical standards, and special data protection.
Coded-based innovative framework for privacy preserving segmentation technology in medical image big data can enable effective sharing and utilization of image-based medical data by safeguarding patient's privacy, significantly enhance the data security and privacy protection level, and the proposing of corresponding policy and legislative coordination strategies offers novel insights and approaches to secure governance in this domain.
