Big data analysis technology mainly includes the latest applied mathematics, cutting-edge computational science and information engineering as the core, and data mining, data warehousing, business intelligence and other intelligent information technology as the means, which can not only greatly improve the traditional medical research technology, but also plays a key role in the development of the latest molecular biology technology.
The adoption of a new technology often means a whole new direction. Just as the use of Roentgen rays in medicine opened up entirely new medical perspectives, the subsequent adoption of new technologies such as CT, MRI, B-US, PETS, etc. have time and again pushed the boundaries of medicine and expanded the horizons of physicians, and today, imaging is an indispensable component. Informatics focuses on the integration of all observable indicators (e.g., age, address, gender, laboratory, treatment, imaging, and all other data that can be observed through existing means), combined with applied mathematics, systems engineering, and reanalysis and reprocessing.
A small number of cases is often not enough to reveal patterns and knowledge, but when the number of cases is large enough, patterns may emerge. So integration into a data warehouse is also necessary. And laws don't just float on the surface of the data, so statistics and data mining become necessary, while the online approach improves speed, and the wizard-like structure based on systems engineering helps stabilize the quality of big data analysis.
When Roentgen rays were introduced into medicine, the situation today must not have been expected. The introduction of KDD into the medical field, in China's vast territory, a huge population base, based on these characteristics of the formation of a huge health information data, just with the traditional method of online can be found in a large number of valuable medical knowledge, and the combination of data mining, data warehousing, systems engineering, the possibility of discovering new knowledge is even more greatly increased.
Health big data analytics technology
Big data analytics technology mainly includes:
Knowledge discovery technology centered on data mining,
Data integration technology centered on data warehousing,
Intelligent decision-making technology centered on business intelligence.
I. Data mining as the core of knowledge discovery technology
Data mining as the core of knowledge discovery technology can be directly mined for new medical knowledge, to help researchers accelerate the achievement of scientific research results, and even major scientific research discoveries.
Using a variety of data mining techniques to explore the laws of data, scientific research design for researchers to provide a scientific basis for scientific research propositions to point out the direction, to ensure the success rate of scientific research.
Data mining is a breakthrough in the traditional means of analysis, for all types of scientific research techniques to provide new technical methods, greatly shorten the scientific research and analysis cycle, in-depth revelation of the potential laws of medicine.
Data mining, also known as knowledge discovery (KDD), is the process of extracting potential, valuable knowledge from a large amount of data. The pattern explored by data mining is a kind of knowledge that exists objectively but is hidden and undiscovered in the data. For example, KDD can directly mine the population with high incidence of diseases, the unknown connection between diseases and symptoms, the influence relationship between lab indicators and the potential influence between lab indicators and diseases, the prediction of unknown test item values, and so on. Inferring unobservable indicators through observable indicators, or inferring expensive or innovative indicators through simple and easy-to-use observable indicators. From the simple to the complex, from the easy to the difficult. In addition, using cluster analysis and factor weighting analysis in research design, we can group data scientifically, examine the different weights of multiple factors, and help determine different research designs such as factor analysis or nested analysis, etc. KDD has been widely used in medicine, providing cutting-edge technology for medical research that cannot be reached by traditional methods, such as:
Cluster analysis, association rule analysis, factor weighting analysis, regression prediction analysis, characterization, extraction, and so on. Weighting analysisRegressionPredictive analysisFeature extraction analysis
The second data warehouse as the core of the data integration technology
The data warehouse technology as the core of the medical data integration system, independently of the existing medical institutions business systems, a new design will be dispersed business systems generated by the inconsistent data collation, transformation, integration, integration to get a comprehensive, efficient and consistent information.
Data warehouse technology also makes it possible and easy to perform online, real-time, in-depth analysis of the entire mass of historical data.
Directly utilizing the existing medical data that has been accumulated allows research costs to be greatly reduced and more research results to be achieved with the same amount of research money.
Applying the integration technology of data warehousing makes it easy to obtain big data research sample data.
Combined with China's huge population base and the vast territory spanning the temperate and tropical zones, the world's largest health information data warehouse can be built, and its comprehensive amount of information is the dream of every medical staff. If it can be cooperated, ****enjoyed, and integrated with countries around the world, it will become a feat on par with the Human Genome Project.
Third, business intelligence as the core of intelligent decision-making technology
Application of mature professional analytical systems to provide consistent and accurate real-time data analysis for all levels of all aspects of health decision-making to provide a reliable basis for the optimization of resources and efficiency, but also from the operational decision-making and management to obtain economic and social benefits.
Applying Business Intelligence (BI) technology to health decision-making analysis enables decision-makers to get rid of the constraints of traditional reports, and to gain a deeper understanding of the data needed in a multi-dimensional way with new and advanced analytical tools, which provide a new and powerful tool for extensive and in-depth analysis.
Professional analytical reports such as cumulative contribution analysis, apportionment percentage analysis, nested ranking analysis, and other specialized analytical reports allow decision makers to have a clear view of the history and status quo, and an easy-to-understand understanding of the cause and effect relationships of various business performances.
Applications of big data analytics for health
Big data analytics for health is used in the following four areas:
Disease and health research
Environment and health research
Pharmaceutical and biotechnology research
Macro decision support for health
Big data analytics will play a special role in the above areas. roles.