The application of new-generation information technologies such as big data, the internet of things(IoT), and cloud computing in the traditional Chinese medicine(TCM)manufacturing industry is gradually deepening, driving the intelligent transformation and upgrading of the TCM industry. At the current stage, there are challenges in understanding the extraction process and its mechanisms in TCM. Online detection technology faces difficulties in making breakthroughs, and data throughout the entire production process is scattered, lacking valuable mining and utilization, which significantly hinders the intelligent upgrading of the TCM industry. Applying data-driven technologies in the process of TCM extraction can enhance the understanding of the extraction process, achieve precise control, and effectively improve the quality of TCM products. This article analyzed the technological bottlenecks in the production process of TCM extraction, summarized commonly used data-driven algorithms in the research and production control of extraction processes, and reviewed the progress in the application of data-driven technologies in the following five aspects: mechanism analysis of the extraction process, process development and optimization, online detection, process control, and production management. This article is expected to provide references for optimizing the extraction process and intelligent production of TCM.
Medicine, Chinese Traditional ; Drugs, Chinese Herbal ; Quality Control ; Big Data ; Algorithms

OBJECTIVE: To explore clinical rules based on the big data of the emergency department of the Second Affiliated Hospital of Guangzhou Medical University, and to establish an integrated platform for clinical research in emergency, which was finally applied to clinical practice. METHODS: Based on the hospital information system (HIS), laboratory information system (LIS), emergency specialty system, picture archiving and communication systems (PACS) and electronic medical record system of the Second Affiliated Hospital of Guangzhou Medical University, the structural and unstructured information of patients in the emergency department from March 2019 to April 2022 was extracted. By means of extraction and fusion, normalization and desensitization quality control, the database was established. In addition, data were extracted from the database for adult patients with pre screening triage level III and below who underwent emergency visits from March 2019 to April 2022, such as demographic characteristics, vital signs during pre screening triage, diagnosis and treatment characteristics, diagnosis and grading, time indicators, and outcome indicators, independent risk factors for poor prognosis in patients were analyzed. RESULTS: (1) The data of 338 681 patients in the emergency department of the Second Affiliated Hospital of Guangzhou Medical University from March 2019 to April 2022 were extracted, including 15 modules, such as demographic information, triage information, visit information, green pass and rescue information, diagnosis information, medical record information, laboratory examination overview, laboratory information, examination information, microbiological information, medication information, treatment information, hospitalization information, chest pain management and stroke management. The database ensured data visualization and operability. (2) Total 140 868 patients with pre-examination and triage level III and below were recruited from the emergency department database. The gender, age, type of admission to the hospital, pulse, blood pressure, Glasgow coma scale (GCS) and other indicators of the patients were included. Taking emergency admission to operating room, emergency admission to intervention room, emergency admission to intensive care unit (ICU) or emergency death as poor prognosis, the poor prognosis prediction model for patients with pre-examination and triage level III and below was constructed. The receiver operator characteristic curve and forest map results showed that the model had good predictive efficiency and could be used in clinical practice to reduce the risk of insufficient emergency pre-examination and triage. CONCLUSIONS The establishment of high-quality clinical database based on big data in emergency department is conducive to mining the clinical value of big data, assisting clinical decision-making, and improving the quality of clinical diagnosis and treatment.
Adult ; Humans ; Big Data ; Emergency Service, Hospital ; Triage/methods* ; Intensive Care Units ; Hospitalization ; Retrospective Studies

Wearable technology, which can continuously and remotely monitor physiological and behavioral parameters by incorporated into clothing or worn as an accessory, introduces a new era for ubiquitous health care. With big data technology, wearable data can be analyzed to help long-term cardiovascular care. This review summarizes the recent developments of wearable technology related to cardiovascular care, highlighting the most common wearable devices and their accuracy. We also examined the application of these devices in cardiovascular healthcare, such as the early detection of arrhythmias, measuring blood pressure, and detecting prevalent diabetes. We provide an overview of the challenges that hinder the widespread application of wearable devices, such as inadequate device accuracy, data redundancy, concerns associated with data security, and lack of meaningful criteria, and offer potential solutions. Finally, the future research direction for cardiovascular care using wearable devices is discussed.
Big Data ; Delivery of Health Care ; Wearable Electronic Devices ; Technology ; Blood Pressure

The common data model (CDM) is an important tool to facilitate the standardized integration of multi-source heterogeneous healthcare big data, enhance the consistency of data semantic understanding, and promote multi-party collaborative analysis. The data collections standardized by CDM can provide powerful support for observational studies, such as large-scale population cohort study. This paper provides an in-depth comparative analysis of the data storage structure, term mapping pattern, and auxiliary tools development of the three international typical CDMs, then analyzes the advantages and limitations of each CDM and summarizes the challenges and opportunities faced in the CDM application in China. It is expected that exploring the advanced technical concepts and practical patterns of foreign countries in data management and sharing will provide references for promoting FAIR (findable, accessible, interoperable, reusable) construction of healthcare big data in China and solving the current practical problems, such as the poor quality of data resources, the low degree of semantization, and the inabilities of data sharing and reuse.
Humans ; Big Data ; China ; Cohort Studies ; Data Collection ; Information Dissemination

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

Objective: To analyze the correlation between loss of smell/taste and the number of real confirmed cases of coronavirus disease 2019 (COVID-19) worldwide based on Google Trends data, and to explore the guiding role of smell/taste loss for the COVID-19 prevention and control. Methods: "Loss of smell" and "loss of taste" related keywords were searched in the Google Trends platform, the data were obtained from Jan. 1 2019 to Jul. 11 2021. The daily and newly confirmed COVID-19 case number were collected from World Health Organization (WHO) since Dec. 30 2019. All data were statistically analyzed by SPSS 23.0 software. The correlation was finally tested by Spearman correlation analysis. Results: A total of data from 80 weeks were collected. The retrospective analysis was performed on the new trend of COVID-19 confirmed cases in a total of 186 292 441 cases worldwide. Since the epidemic of COVID-19 was recorded on the WHO website, the relative searches related to loss of smell/taste in the Google Trends platform had been increasing globally. The global relative search volumes of "loss of smell" and "loss of taste" on Google Trends was 10.23±2.58 and 16.33±2.47 before the record of epidemic while 80.25±39.81 and 80.45±40.04 after (t value was 8.67, 14.43, respectively, both P<0.001). In the United States and India, the relative searches for "loss of smell" and "loss of taste" after the record of epidemic were also much higher than before (all P<0.001). The correlation coefficients between the trend of weekly new COVID-19 cases and the Google Trends of "loss of smell" in the global, United States, and India was 0.53, 0.76, and 0.82 respectively (all P<0.001), the correlation coefficients with Google Trends of "loss of taste" was 0.54, 0.78, and 0.82 respectively (all P<0.001). The lowest and highest point of loss of smell/taste search curves of Google Trends in different periods appeared 7 to 14 days earlier than that of the weekly newly COVID-19 confirmed cases curves, respectively. Conclusions: There is a significant positive correlation between the number of newly confirmed cases of COVID-19 worldwide and the amount of keywords, such as "loss of smell" and "loss of taste", retrieved in Google Trends. The trend of big data based on Google Trends might predict the outbreak trend of COVID-19 in advance.
Ageusia ; Big Data ; COVID-19 ; Disease Outbreaks ; Humans ; Internet ; Retrospective Studies ; Smell ; United States