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A Cooperated Data Management Platform for Coronary Heart Disease Early Identification and Risk Warning Research

机译：冠心病早期识别和风险预警研究的协同数据管理平台

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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.
机译：大数据驱动技术和深度学习方法正在引起人们对冠心病（CHD）的早期识别和风险预警研究的关注。冠心病是威胁人类健康和生命的常见慢性疾病之一。队列研究方法和机器学习方法通​​常用于识别以精确定位患者的目标。据我们所知，文献主要侧重于如何建立和优化识别和警告模型或同类研究，而忽略了数据管理。为了促进冠心病的早期识别和风险预警研究，我们为大患者数据和冠心病早期识别模型数据提供了一个协作的数据管理平台。根据模型数据的特点，提出了SMR（Samples-Model-Results）数据链的概念，以描述训练数据，模型与模型评估结果之间的关系。提取了关于冠心病患者队列和冠心病早期识别模型的概念图式，它们是与系统无关的表示。为了针对DBMS，基于概念性数据模型设计了与系统有关的逻辑数据模式。进行了有关关系数据库和基于NoSQL数据库的解决方案效率的实验。为了有效地管理冠心病早期识别模型数据，我们提出了模型版本来代表考虑模型生命周期的模型之间的关系。建立模型树，并设计查询算法以执行CHD早期识别模型的血统管理。基于协作数据管理平台的架构设计，为冠心病早期识别和风险预警研究人员实施了有效的患者数据视觉探索服务，队列研究服务和冠心病早期识别模型选择，模型比较和模型数据视觉探索服务。

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