Research On The Application Model Of Big Data In Healthcare

[Purpose] The application of big data in healthcare is an important starting point for the construction of "Healthy China 2030" and the deepening of the reform of the medical and health system.To consolidate the foundation of China’s big data application and promote the development and utilization of big data in healthcare,the big data application model in healthcare is studied in this thesis,which builds the overall framework of the big data application model in healthcare,discusses the application process and the key technologies in healthcare,researches the big data analysis methods and the development trends,builds the big data application system model and the application system catalogs,and analyzes the big data guarantee system and recommendations.[Methods] The research methods used in this thesis include the literature research method,the questionnaire survey method,the interview method,and the expert consultation method.To provide theoretical basis for this research,the literature research method is used to refer to the related literatures about the application process,the key technologies,and the analysis methods and applications of big data in healthcare,etc.To provide practical support for this research,the questionnaire survey method and the interview method are used to understand the current status and the application requirements of big data in healthcare institutions.The expert consultation method is used to consult and discuss the big data application model in healthcare,and modify it according to the opinions of the experts.[Results](1)The meaning of the application model is defined in this thesis.And the overall framework of the big data application model in healthcare is constructed.(2)The application process covering the collection,preprocessing,storage,analysis,and visualization of big data in healthcare is established.And the key technologies and products in the process of big data application in healthcare are discussed,including the data extraction tool of Sqoop,the distributed storage system of GFS and HDFS,the database management systems of No SQL and New SQL,and the big data programming model of Map Reduce.(3)The big data analysis method framework in healthcare is constructed,including the target layer,the data layer,the tool and platform layer,the method layer,and the application layer.The principles,advantages,limitations,and the application in healthcare of ten big data analysis methods are studied,such as artificial neural networks and random forests,etc.And the development trend of analytical methods in the environment of big data is discussed,including migration to the big data framework,acceleration using GPUs,and targeted optimization of algorithms.(4)The model of the big data application system in healthcare is constructed.The eight major application scenarios of big data in healthcare are analyzed,including public health,medical services,healthcare security,drug management,population monitoring and family development,comprehensive management,industrial development,and "Internet+".The application system catalog covering 115 big data applications in healthcare is constructed,which explains detailly the main functions,the data composition,the data sources,and the application subjects of each application.(5)The guarantee system and the application suggestions of big data in healthcare are researched.[Conclusions] The research results of the overall framework of the big data application model in healthcare,the application process and the key technologies,the analysis method framework and the analysis methods,the application system model and application system catalog,and the application guarantee and recommendation,etc,can provide references and theoretical supports for the relevant government departments such as the Health Commission at all levels to promote the application and development of big data.It also can provide people engaging in healthcare big data analysis and application guidance on selecting methods and technologies,understanding and mining application requirements,etc.