In the era of big data, neuroimaging and algorithmic analyses have propelled brain science research and brain mapping. Acupuncture, widely recognized as an effective surface stimulation therapy, has demonstrated therapeutic efficacy for various brain conditions such as stroke and depression. However, the mechanisms linking acupuncture to brain function and its modulatory effects on brain activity require systematic exploration. Additionally, there is an urgent need to scientifically reinterpret traditional meridian theory and enhance its clinical applicability. Therefore, we propose the initiative of constructing a "brain mapping atlas of meridian, collateral and body surface stimulation" to explore the patterns linking the therapeutic effects of stimulating the twelve meridians, eight extraordinary vessels, divergent channels, collateral channels, sinew channels, and skin regions to brain function. This initiative aims to provide a scientific interpretation of traditional Chinese medicine meridian theory and enhance its practical applicability. This paper begins by reviewing the current state of brain mapping. It then summarizes existing research on the relationship between acupuncture and the brain, highlighting the necessity of constructing this atlas. The paper further analyzes the methodologies and technical challenges involved. Finally, the potential applications of the brain mapping atlas of meridian, collateral and body surface stimulation, and its main significance in advancing traditional meridian theory to keep pace with the times are prospected.
Humans ; Acupuncture Therapy ; Meridians ; Big Data ; Brain/physiology* ; Brain Mapping

Biomedical big data, characterized by its massive scale, multi-dimensionality, and heterogeneity, offers novel perspectives for disease research, elucidates biological principles, and simultaneously prompts changes in related research methodologies. Biomedical ontology, as a shared formal conceptual system, not only offers standardized terms for multi-source biomedical data but also provides a solid data foundation and framework for biomedical research. In this review, we summarize enrichment analysis and deep learning for biomedical ontology based on its structure and semantic annotation properties, highlighting how technological advancements are enabling the more comprehensive use of ontology information. Enrichment analysis represents an important application of ontology to elucidate the potential biological significance for a particular molecular list. Deep learning, on the other hand, represents an increasingly powerful analytical tool that can be more widely combined with ontology for analysis and prediction. With the continuous evolution of big data technologies, the integration of these technologies with biomedical ontologies is opening up exciting new possibilities for advancing biomedical research.
Deep Learning ; Biological Ontologies ; Humans ; Big Data ; Biomedical Research

Viral infectious diseases, characterized by their intricate nature and wide-ranging diversity, pose substantial challenges in the domain of data management. The vast volume of data generated by these diseases, spanning from the molecular mechanisms within cells to large-scale epidemiological patterns, has surpassed the capabilities of traditional analytical methods. In the era of artificial intelligence (AI) and big data, there is an urgent necessity for the optimization of these analytical methods to more effectively handle and utilize the information. Despite the rapid accumulation of data associated with viral infections, the lack of a comprehensive framework for integrating, selecting, and analyzing these datasets has left numerous researchers uncertain about which data to select, how to access it, and how to utilize it most effectively in their research.This review endeavors to fill these gaps by exploring the multifaceted nature of viral infectious diseases and summarizing relevant data across multiple levels, from the molecular details of pathogens to broad epidemiological trends. The scope extends from the micro-scale to the macro-scale, encompassing pathogens, hosts, and vectors. In addition to data summarization, this review thoroughly investigates various dataset sources. It also traces the historical evolution of data collection in the field of viral infectious diseases, highlighting the progress achieved over time. Simultaneously, it evaluates the current limitations that impede data utilization.Furthermore, we propose strategies to surmount these challenges, focusing on the development and application of advanced computational techniques, AI-driven models, and enhanced data integration practices. By providing a comprehensive synthesis of existing knowledge, this review is designed to guide future research and contribute to more informed approaches in the surveillance, prevention, and control of viral infectious diseases, particularly within the context of the expanding big-data landscape.
Big Data ; Humans ; Virus Diseases/virology* ; Artificial Intelligence

Big Data ; Humans ; Data Management

Medical equipment management plays a crucial role in enhancing the quality and efficiency of healthcare services. However, traditional management approaches are increasingly inadequate to meet the growing demands of modern healthcare. As intelligent operation and maintenance (O&M) models based on big data, the Internet of Things (IoT), and artificial intelligence (AI) technologies develop, it is imperative to explore their application in medical equipment management. This paper reviews the technical overview of intelligent O&M and discusses the algorithms and challenges of intelligent O&M models based on different technologies. It also proposes issues that need improvement in intelligent O&M models, aiming to provide valuable references for the future development of medical equipment management.
Artificial Intelligence ; Algorithms ; Internet of Things ; Equipment and Supplies ; Big Data

To explore the characteristics of big data of patients with allergic rhinitis, including the time, population and spatial distribution of allergic rhinitis in Beijing from 2016 to 2021, so as to provide reference for the prevention and treatment of this disease. Descriptive epidemiological methods were used to analyze the distribution (including gender, age and location)and trend of allergic rhinitis patients in 30 pilot hospitals from January 2016 to December 2021, T test and Kruskal-Wallis rank sum test were used to test the statistical differences. The results showed that the number of patients with allergic rhinitis in 30 hospitals increased year by year from 2016 to 2019, with an increase of 97.9%. In 2020, the number of patients decreased. In 2021, the number of visits returned to the pre-epidemic level (461 332); The number of patients with allergic rhinitis was the highest in September, with a seasonal index of 177.6%, while the lowest number was in February, accounting for only 47.2%; a significant difference was observed in the number of patients in different age groups(H=45 319.48, P<0.05), and patients under 15 years old accounted for the highest proportion(819 284 visits); There were significant differences between patients of different genders in the 45-59 year old group (t=-4.26, P<0.05).There were relatively more patients with allergic rhinitis in Dongcheng District(31.1%) than in Huairou District and Miyun District (0.4%). In conclusion, since 2016, the number of patients increased significantly, with a varied trend in different seasons. Most patients were children. There were more patients in the central urban area than in the outer suburbs.
Child ; Humans ; Female ; Male ; Adolescent ; Middle Aged ; Beijing/epidemiology* ; Big Data ; Epidemics ; Hospitals ; Rhinitis, Allergic/epidemiology*

Medical test results are indispensable and important tools in diagnosis and treatment services. It is necessary to promote the homogenization of test results first, because homogenization is the basis for mutual recognition of test results. Mutual recognition of medical test results can help share resources among medical institutions, provide more reliable test results for early prevention, screening and treatment of diseases, and reduce repeated tests, thus improving people's medical experience. In recent years, with the deepening of medical system reform and the promotion of graded diagnosis and treatment, governments have continuously introduced policies of mutual recognition of test results around country. However, homogenization is a prerequisite for mutual recognition of test results, with the emergence of intelligent medicine in the era of internet big data, opportunities and challenges coexist in the development of homogeneity management. In the future, the homogeneity of medical test results will present a trend of digitalization, automation, informatization and intelligence.
Humans ; Big Data ; Government ; Internet

Wound age estimation is the core content in the practice of forensic medicine. Accurate estimation of wound age is a scientific question that needs to be urgently solved by forensic scientists at home and abroad. Metabolomics techniques can effectively detect endogenous metabolites produced by internal or external stimulating factors and describe the dynamic changes of metabolites in vivo. It has the advantages of strong operability, high detection efficiency and accurate quantitative results. Machine learning algorithm has special advantages in processing high-dimensional data sets, which can effectively mine biological information and truly reflect the physiological, disease or injury state of the body. It is a new technical means for efficiently processing high-throughput big data. This paper reviews the status and advantages of metabolomic techniques combined with machine learning algorithm in the research of wound age estimation, and provides new ideas for this research.
Algorithms ; Machine Learning ; Forensic Medicine ; Metabolomics ; Big Data

To explore the characteristics of big data of patients with allergic rhinitis, including the time, population and spatial distribution of allergic rhinitis in Beijing from 2016 to 2021, so as to provide reference for the prevention and treatment of this disease. Descriptive epidemiological methods were used to analyze the distribution (including gender, age and location)and trend of allergic rhinitis patients in 30 pilot hospitals from January 2016 to December 2021, T test and Kruskal-Wallis rank sum test were used to test the statistical differences. The results showed that the number of patients with allergic rhinitis in 30 hospitals increased year by year from 2016 to 2019, with an increase of 97.9%. In 2020, the number of patients decreased. In 2021, the number of visits returned to the pre-epidemic level (461 332); The number of patients with allergic rhinitis was the highest in September, with a seasonal index of 177.6%, while the lowest number was in February, accounting for only 47.2%; a significant difference was observed in the number of patients in different age groups(H=45 319.48, P<0.05), and patients under 15 years old accounted for the highest proportion(819 284 visits); There were significant differences between patients of different genders in the 45-59 year old group (t=-4.26, P<0.05).There were relatively more patients with allergic rhinitis in Dongcheng District(31.1%) than in Huairou District and Miyun District (0.4%). In conclusion, since 2016, the number of patients increased significantly, with a varied trend in different seasons. Most patients were children. There were more patients in the central urban area than in the outer suburbs.
Child ; Humans ; Female ; Male ; Adolescent ; Middle Aged ; Beijing/epidemiology* ; Big Data ; Epidemics ; Hospitals ; Rhinitis, Allergic/epidemiology*

Medical test results are indispensable and important tools in diagnosis and treatment services. It is necessary to promote the homogenization of test results first, because homogenization is the basis for mutual recognition of test results. Mutual recognition of medical test results can help share resources among medical institutions, provide more reliable test results for early prevention, screening and treatment of diseases, and reduce repeated tests, thus improving people's medical experience. In recent years, with the deepening of medical system reform and the promotion of graded diagnosis and treatment, governments have continuously introduced policies of mutual recognition of test results around country. However, homogenization is a prerequisite for mutual recognition of test results, with the emergence of intelligent medicine in the era of internet big data, opportunities and challenges coexist in the development of homogeneity management. In the future, the homogeneity of medical test results will present a trend of digitalization, automation, informatization and intelligence.
Humans ; Big Data ; Government ; Internet