Peili Yang, H Zhao, Xuezhen Yin, Jian Ye, Lingfeng Yang, Jimin Liang
{"title":"冠心病早期识别与风险预警协同数据管理平台研究","authors":"Peili Yang, H Zhao, Xuezhen Yin, Jian Ye, Lingfeng Yang, Jimin Liang","doi":"10.1109/COMPSAC.2018.10327","DOIUrl":null,"url":null,"abstract":"Big data-driven technologies and deep learning approaches are being drawn much attention to Coronary Heart Disease(CHD) early identification and risk warning research. CHD is one of the common chronic diseases that threaten the health and life of people. Cohort study method and machine learning method are often used to identify to target the patients precisely. To the best of our knowledge, the literatures mostly focused on how to establish and optimize the identification and warning models or the cohort study, while overlooking the data management. To promote the early identification and risk warning research of CHD, we contribute a cooperated data management platform in regards to the big patient data and big CHD early identification model data. According to the characteristics of the model data, we propose the SMR(Samples-Model-Results) data chain conception to describe the relationship among the training data, model and the model evaluation result. The conceptual schema about CHD patient cohort and CHD early identification model are abstracted which are system-independent representations. To target the DBMS, system-dependent logical data schemas are designed based on the conceptual data model. The experiments about the efficiency of relational database and NoSQL database based solutions are conducted. To manage the CHD early identification model data effectively, we propose the model version to represent the relationship between the models considering the modeling lifecycle. The model tree is established and the query algorithms are designed to perform the lineage management of the CHD early identification models. The effective patient data visual exploration services, cohort study services and CHD early identification model selection, model comparison and model data visual exploration services are implemented for CHD early identification and risk warning researchers based on the architecture design of the Cooperated Data Management Platform.","PeriodicalId":74502,"journal":{"name":"Proceedings : Annual International Computer Software and Applications Conference. COMPSAC","volume":"28 1","pages":"731-736"},"PeriodicalIF":0.0000,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"A Cooperated Data Management Platform for Coronary Heart Disease Early Identification and Risk Warning Research\",\"authors\":\"Peili Yang, H Zhao, Xuezhen Yin, Jian Ye, Lingfeng Yang, Jimin Liang\",\"doi\":\"10.1109/COMPSAC.2018.10327\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Big data-driven technologies and deep learning approaches are being drawn much attention to Coronary Heart Disease(CHD) early identification and risk warning research. CHD is one of the common chronic diseases that threaten the health and life of people. Cohort study method and machine learning method are often used to identify to target the patients precisely. To the best of our knowledge, the literatures mostly focused on how to establish and optimize the identification and warning models or the cohort study, while overlooking the data management. To promote the early identification and risk warning research of CHD, we contribute a cooperated data management platform in regards to the big patient data and big CHD early identification model data. According to the characteristics of the model data, we propose the SMR(Samples-Model-Results) data chain conception to describe the relationship among the training data, model and the model evaluation result. The conceptual schema about CHD patient cohort and CHD early identification model are abstracted which are system-independent representations. To target the DBMS, system-dependent logical data schemas are designed based on the conceptual data model. The experiments about the efficiency of relational database and NoSQL database based solutions are conducted. To manage the CHD early identification model data effectively, we propose the model version to represent the relationship between the models considering the modeling lifecycle. The model tree is established and the query algorithms are designed to perform the lineage management of the CHD early identification models. The effective patient data visual exploration services, cohort study services and CHD early identification model selection, model comparison and model data visual exploration services are implemented for CHD early identification and risk warning researchers based on the architecture design of the Cooperated Data Management Platform.\",\"PeriodicalId\":74502,\"journal\":{\"name\":\"Proceedings : Annual International Computer Software and Applications Conference. 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A Cooperated Data Management Platform for Coronary Heart Disease Early Identification and Risk Warning Research
Big data-driven technologies and deep learning approaches are being drawn much attention to Coronary Heart Disease(CHD) early identification and risk warning research. CHD is one of the common chronic diseases that threaten the health and life of people. Cohort study method and machine learning method are often used to identify to target the patients precisely. To the best of our knowledge, the literatures mostly focused on how to establish and optimize the identification and warning models or the cohort study, while overlooking the data management. To promote the early identification and risk warning research of CHD, we contribute a cooperated data management platform in regards to the big patient data and big CHD early identification model data. According to the characteristics of the model data, we propose the SMR(Samples-Model-Results) data chain conception to describe the relationship among the training data, model and the model evaluation result. The conceptual schema about CHD patient cohort and CHD early identification model are abstracted which are system-independent representations. To target the DBMS, system-dependent logical data schemas are designed based on the conceptual data model. The experiments about the efficiency of relational database and NoSQL database based solutions are conducted. To manage the CHD early identification model data effectively, we propose the model version to represent the relationship between the models considering the modeling lifecycle. The model tree is established and the query algorithms are designed to perform the lineage management of the CHD early identification models. The effective patient data visual exploration services, cohort study services and CHD early identification model selection, model comparison and model data visual exploration services are implemented for CHD early identification and risk warning researchers based on the architecture design of the Cooperated Data Management Platform.