Big data analysis technology will play a special role in the above aspects.
I disease and health research
In the research of disease and health, we can divide it into three sub-aspects: health research, sub-health research and disease research.
1, health research
China is a vast multi-ethnic country. People in different regions and different populations have different genes and health indicators, and people in the same region and the same population have different health standards at different genders and ages. In-depth study and analysis of the health laws of the above population is of great guiding significance to medical care, health promotion and disease prevention. For example:
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1.2 When making reference values for different populations in different regions, we can further analyze the differences of health indicators and weight ratios in different genders, ages and seasons, so as to improve the comprehensive and systematic health reference values suitable for China people.
1.3 The inherent balance of human existence makes every observable data have its own unique laws, and simple laws such as calcium and phosphorus constants can only be discovered based on experience. Therefore, the application of big data analysis technology such as data mining can actively discover complex and systematic human medical laws, greatly improve the technical level of disease prevention, treatment and prognosis prediction, and also have a more scientific judgment basis for sub-health, and understand the process of gradual imbalance from health to sub-health.
1.4 analyze the health data of pregnant women, postpartum women and newborns, study the health laws of pregnant women and newborns, develop the health assessment model of pregnant women and newborns, and give more scientific guidance to the health care of pregnant women and newborns.
1.5 Analyze and mine the physical examination data of children's growth, study the laws of children's health, and develop evaluation models of children's growth and factors respectively, so as to adapt to the characteristics of China's vast territory and large population and give more scientific guidance to children's growth and development.
1.6 Analyze and study the health data of the elderly, study their health characteristics, develop the evaluation model and influencing factors of their health, and give more scientific guidance to their health preservation.
1.7 deeply analyze the mental and psychological data of healthy people, formulate the mental and psychological reference standards of healthy people, develop the evaluation model of healthy mental and psychological and influencing factors, and give more scientific mental and psychological health care guidance.
2. Sub-health research
The World Health Organization calls the state in which the body has no organic pathological changes, but some functional changes, also known as "sub-health state", which mainly includes: functional changes, not organic pathological changes; Signs change, but the existing medical technology can not find the lesion; Poor quality of life and long-term low health level; There are unhealthy signs of chronic diseases outside the lesion site.
In-depth analysis and research on sub-health is of great significance for maintaining healthy state, preventing and correcting sub-health state and preventing diseases. For example:
2. 1 Study the relationship between sub-health and diseases. To study the weight of various observable indicators (physical examination data) in sub-health and their distribution in different regions and populations. Time series and linear/nonlinear regression were used to study the correlation between sub-health observation indexes. Through the data mining of sub-health physical examination, this paper analyzes the influencing factors leading to diseases, establishes an evaluation model to predict risks, and then establishes a disease prediction model.
2.2 Study the relationship between sub-health and health. To study the latest distribution of healthy people and sub-healthy people by analyzing the geographical, occupational, age and other factors of the physical examination population. Different people have different geographical environment and living habits. After adding relevant external data (such as occupation, diet, habits, personality, hobbies, etc.). In addition to sub-health medical indicators, we can also find out the influence of comprehensive factors on sub-health, and their respective weights and correlations, so as to explore the causes of sub-health and play a guiding role in the prevention and treatment of sub-health.
2.3 Study the treatment and prognosis of sub-health. Through the analysis of sub-health treatment and prognosis data, we can evaluate the treatment effect and the best treatment scheme, further carry out the research on sub-health treatment and prognosis, and study its relationship with diseases.
2.4 Research on mental and psychological sub-health. For example, the common mental sub-health states such as neurasthenia, depression, anxiety and compulsion are summarized, analyzed and excavated, so as to derive new knowledge discovery of mental sub-health and explore the causes of mental diseases, which will play a guiding role in the prevention and treatment of mental diseases.
2.5 Combining the data of hospitalization and community health management, we analyzed the weight of factors, extracted the characteristics of multiple factors, and finally formed a model to guide treatment. The ideal situation is individualized evaluation model, and a special prediction model is established for each patient.
3. Disease research
In China, diseases that seriously endanger people's health include:
Infectious diseases, such as tuberculosis, AIDS, SARS, avian influenza, influenza A H 1N 1, etc.
Chronic non-communicable diseases, such as malignant tumor, cerebrovascular disease, heart disease, diabetes, etc.
Mental and psychological diseases;
Congenital defects in children.
The research and analysis of medical data and related data of patients with various diseases is of great value for the prevention and treatment of various diseases. For example:
3. 1 research on infectious diseases, such as tuberculosis, AIDS, SARS, avian influenza and influenza A (H 1N 1). Using data mining technology to analyze the data of infectious diseases, find out the incidence law of infectious diseases, reveal the causes of infectious diseases, further explore the variation law of infectious diseases, and establish the prediction model of infectious diseases.
3.2 Research on chronic non-communicable diseases, such as malignant tumor, cerebrovascular disease, heart disease, diabetes and other diseases. Using data warehouse technology and data mining technology to analyze the data of chronic common diseases, find out the incidence law of chronic common diseases, explore the causes of chronic common diseases, further explore the complications law of chronic common diseases, scientifically evaluate the efficacy of various treatment schemes, and establish a prediction model of chronic common diseases.
3.3 Research on mental illness and mental illness. Using data warehouse technology, data mining technology and mathematical statistics technology, this paper analyzes the data of mental illness and mental illness, finds out the main factors affecting mental illness and mental illness from a wide variety of collections, explores the causes of mental illness and mental illness from heredity, acquired influence and pathology, scientifically evaluates the curative effects of various treatment schemes, and establishes a prediction model of mental illness and mental illness.
3.4 Study on Children's Birth Defects. Using big data analysis technology to analyze the data of children's birth defects, find out the main factors affecting children's birth defects from a wide range of large variables, explore the causes of children's birth defects from many aspects such as environment, heredity and pathology, and establish a prediction model of children's birth defects.
3.5 Analyze the statistical differences between outpatient and inpatient data online, find positive cases, provide materials for research, and provide ideas and preparations for the pre-experiment of scientific research. Multi-dimensional analysis and mining of hospitalization data can reach the level of a single disease horizontally and include all observable data vertically, and the collected knowledge is likely to inspire medical experts to make new discoveries.
3.6 Online analysis of different treatment methods and therapeutic effects. Combined with a large number of collected data, comprehensive analysis, try to fully understand the clinical effect of treatment in advance.
3.7 On-line analysis of drug treatment effect, evaluation of treatment effect, side effects and impact on other diseases. Combined with a large number of collected data, comprehensive analysis, try to fully understand new drugs and old drugs in advance. At present, adverse drug reactions mainly depend on doctors' notification, which largely depends on doctors' professionalism and sensitivity, and the use of data mining and knowledge discovery in the database can greatly improve this work.
Second, the environment and health research
The damage caused by environmental factors to health is more complicated than other health damage, which is microscopic, chronic, long-term and irreversible. The impact of environmental health is closely related to public interests. If it is not handled properly, environmental health damage will turn into social and economic problems. The research on environment and public health is based on the research on the sustainable development of human ecosystem, caring about the health and safety of human beings now and in the future, paying attention to the influence of social and economic activities on human physical and mental health from the perspective of environmental research, and exploring preventive measures for environmental changes that endanger human health.
The application of big data analysis technology in environmental health research mainly includes case discovery, pathogenesis and clinical treatment research, prevention and control of various environmental epidemics in pollution sources and pollution path control research. For example:
1. Apply big data analysis technology to study the impact of environmental factors on health, implement integrated monitoring of environment and health, and realize national data sharing.
2. Apply big data analysis technology to study the impact of environmental pollution on children, so as to solve the unhealthy and rapidly increasing diseases caused by the environment, and then give children special attention to the environment and health guidance.
3. Apply big data analysis technology to prevent and predict occupational diseases and occupational frequently-occurring diseases. In-depth analysis of the distribution and severity of various occupations and occupational diseases. It includes not only occupational diseases in the traditional sense, but also the distribution of different diseases in different occupations and their weight in the etiology. In addition, we can also analyze the exposure characteristics of different occupations and then study the reasons.
4. Use big data analysis technology to carry out air pollution research, and significantly improve the incidence of respiratory tract and allergic diseases in urban people.
5. Apply big data analysis technology to carry out research on noise pollution impairing children's hearing and interfering with their learning ability.
6. Use big data analysis technology to carry out research on the increase of obesity incidence caused by the development of fast food industry, especially the impact of unreasonable nutrition on children's health.
7. Apply big data analysis technology to study the potential impact of the application of transgenic biotechnology on natural organisms and human genes.
Three. Pharmaceutical biotechnology and health
Biotechnology covers all fields of life science, and medical biotechnology is an important part of biotechnology. Population, food, health, environment and resources are all closely related to them. The most remarkable feature of medical biotechnology is the introduction of a large number of new ideas, new technologies, new materials, new methods and new products into medical research and medical care. Such as brand-new medical imaging technology, genetic engineering technology, microelectronics technology, stem cell engineering technology, tissue engineering technology, nanotechnology, biochip technology, cloning technology, enzyme engineering technology, cell engineering technology, fermentation engineering technology, protein engineering technology, biomedical engineering technology, genome and protein group technology, bioinformatics technology and traditional Chinese medicine technology. And their products will greatly improve the level of disease prevention, diagnosis, treatment and drug design and development, as well as emergency (.
Bioinformatics with big data analysis technology as the core plays a unique role in medical biotechnology composed of many new technologies. For example:
1. Use bioinformatics technology to store and obtain bioinformatics.
2. Using bioinformatics technology to compare, sequence and splice gene sequences.
3. Using bioinformatics technology to predict genes.
4. Using bioinformatics technology to analyze biological evolution and phylogeny.
5. Using bioinformatics technology to predict the structure of protein and RAN.
6. Using bioinformatics technology for molecular design and drug design.
7. Using bioinformatics technology for tumor classification and gene analysis.
8. Using bioinformatics technology to carry out research and genetic analysis of mental diseases at the biomolecular level.
9. Using bioinformatics technology to study H 1N 1 and other infectious diseases at the biomolecular level.
Fourth, health macro-decision support.
Health macro-decision support system is a comprehensive health information platform with data warehouse as the data center, data mining as the technical core and business intelligence as the presentation tool. It can be built on hospitals, regional health systems, national health systems and other health systems at all levels, providing intelligent decision-making systems for health departments at all levels, deeply understanding the history and present of the health system, grasping the future of the business development of the health system, evaluating the business performance of various departments within the health system, helping decision makers at all levels to provide the best implementation plan, so that they can have a pair of discerning eyes and clearly understand the changing trends and business gains and losses of all aspects of the system. Make the evaluation, assessment and reward of all departments of the system more scientific, fair and objective, make the relationship between all levels in the system more harmonious, give full play to the potential of all departments, and improve the overall business level and economic benefits of the system. Using business intelligence to assist decision-making can provide all kinds of valuable information, the correlation of various events, and analyze all kinds of health information from different angles, such as basic vaccination data, infectious disease reports and so on.
The above is what Bian Xiao shared for you about the application of big data analysis in disease and health research. For more information, you can pay attention to more dry goods sharing of global ivy.