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

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

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

The micronucleomics test can comprehensively display a variety of harmful endpoints, such as DNA damage and repair, chromosome breakage or loss and cell growth inhibition, with fast, simple and economical feature. Micronucleomics is not only widely used in the comprehensive assessment of the types and modes of genetic action of exogenous chemicals (such as drugs, food additives, cosmetics, environmental pollutants, etc.), but also plays an important role in the screening and risk assessment of cancer population at high risk. However, the traditional micronucleomics image counting method has the characteristics of time-consuming, low accuracy, and high cost, which cannot meet the current analysis requirements of large-scale, multi-index, rapidity, high precision and visualization. In recent years, with the rapid development of the era of precision medicine based on big data, visualized analysis of new micronucleomics based on machine learning and detection strategies based on deep learning have shown a good application prospect. This review, based on the application value of micronucleomics, systematically compares the traditional and new artificial intelligence counting of micronucleus images, and discusses the future direction of micronucleus image detection.
Artificial Intelligence ; Big Data ; Humans ; Machine Learning ; Precision Medicine

From the perspective of data users, ensuring the relevance and reliability of big data in healthcare and medicine via assessments on data appropriateness is a prerequisite for generating high-quality real-world evidence, which could guarantee good representativeness and generalizability of real-world studies. This review summarized the quality dimensions, definitions, evaluation indexes and calculating methods of assessment on the appropriateness of real-world data (RWD) according to guidance from different countries and international organizations, as well as exploring the opportunities and challenges for better assessing RWD appropriateness.
Big Data ; Delivery of Health Care ; Humans ; Reproducibility of Results

With the promotion and application of big medical data, non-interventional real-world evidence (RWE) has been used by regulators to assess the effectiveness of medical products. This paper briefly introduces the latest progress and research results of the RCT DUPLICATE Initiative launched by the research team of Harvard University in 2018 and summarizes relevant research experience based on the characteristics of China's medical service to provide inspiration and reference for domestic scholars to conduct related RWE research in the future.
Humans ; Randomized Controlled Trials as Topic ; Cognition ; Big Data ; Universities

As traditional data management model cannot effectively manage the massive data in traditional Chinese medicine(TCM) due to the uncertainty of data object attributes as well as the diversity and abstraction of data representation, a management strategy for TCM data based on big data technology is proposed. Based on true characteristics of TCM data, this strategy could solve the problems of the uncertainty of data object attributes in TCM information and the non-uniformity of the data representation by using modeless properties of stored objects in big data technology. Hybrid indexing mode was also used to solve the conflicts brought by different storage modes in indexing process, with powerful capabilities in query processing of massive data through efficient parallel MapReduce process. The theoretical analysis provided the management framework and its key technology, while its performance was tested on Hadoop by using several common traditional Chinese medicines and prescriptions from practical TCM data source. Result showed that this strategy can effectively solve the storage problem of TCM information, with good performance in query efficiency, completeness and robustness.
Big Data ; Information Storage and Retrieval ; methods ; Medicine, Chinese Traditional

Lonicerae Japonicae Flos and Artemisiae Annuae Herba(LA or Jinqing) alcohol precipitation has various process parameters and complex process mechanism, and is one of the key units for manufacturing Reduning Injection. In order to identify the critical process parameters(CPPs) affecting the weight of the extract produced from the alcohol precipitation process, 259 batches of historical production data from 2017 to 2018 were collected, with a total of 829 318 data points. These data showed characteristics of large data, such as a large data volume, a low value density, and diverse sources. The data cleaning and feature extraction were first performed, and 48 feature variables were selected. The original data points were reduced to 9 936. Then, a combination of Pearson correlation analysis and grey correlation analysis were used to screen out 15 potential critical process parameters(pCPPs). After that, the partial least squares(PLS) was used in prediction of the weight of the extract, proving that the performance of predictive model based on 15 pCMAs is equivalent to that of predictive model based on 48 feature variables. The variable importance in projection(VIP) index was used to identify 9 CPPs, including 2 alcohol precipitation supernatant volume parameters, 4 initial extract weight parameters and 3 added alcohol volume parameters. As a result, the number of data points was 1 863, accounting for 0.28% of the original data. The big data analysis approach from a holistic point of view can effectively increase the value density of the original data. The critical process parameters obtained can help to accurately describe the quality transfer mechanism of the Jinqing alcohol precipitation process.
Alcohols ; Big Data ; Drugs, Chinese Herbal/chemistry* ; Solvents ; Technology, Pharmaceutical

Heart failure is a serious condition with high prevalence and mortality. The application of the novel big data analysis in heart failure can improve the management of this condition,especially in terms of diagnosis,classification,and prognostic prediction. This articles reviews relevant literature and validates the role of big data analysis for heart failure patients.
Big Data ; Heart Failure ; diagnosis ; therapy ; Humans ; Prognosis

The analysis of big data in medical field cannot be isolated from the high quality clinical database, and the construction of first aid database in our country is still in the early stage of exploration. This paper introduces the idea and key technology of the construction of multi-parameter first aid database. By combining emergency business flow with information flow, an emergency data integration model was designed with reference to the architecture of the Medical Information Mart for Intensive Care III (MIMIC-III), created by Computational Physiology Laboratory of Massachusetts Institute of Technology (MIT), and a high-quality first-aid database was built. The database currently covers 22 941 medical records for 19 814 different patients from May 2015 to October 2017, including relatively complete information on physiology, biochemistry, treatment, examination, nursing, etc. And based on the database, the first First-Aid Big Data Datathon event, which 13 teams from all over the country participated in, was launched. The First-Aid database provides a reference for the construction and application of clinical database in China. And it could provide powerful data support for scientific research, clinical decision making and the improvement of medical quality, which will further promote secondary analysis of clinical data in our country.
Big Data ; Critical Care ; Databases, Factual ; Humans ; Medical Informatics