冠心病早期识别与风险预警协同数据管理平台研究

Peili Yang, H Zhao, Xuezhen Yin, Jian Ye, Lingfeng Yang, Jimin Liang
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引用次数: 1

摘要

大数据驱动技术和深度学习方法在冠心病早期识别和预警研究中受到越来越多的关注。冠心病是威胁人们健康和生命的常见慢性疾病之一。通常采用队列研究方法和机器学习方法来精确识别目标患者。据我们所知,文献大多集中在如何建立和优化识别和预警模型或队列研究,而忽略了数据管理。为促进冠心病的早期识别和风险预警研究,我们针对患者大数据和冠心病早期识别大模型数据贡献了协同数据管理平台。根据模型数据的特点,提出了SMR(Samples-Model-Results)数据链的概念来描述训练数据、模型和模型评价结果之间的关系。抽象了冠心病患者队列的概念图式和冠心病早期识别模型,它们是系统无关的表征。针对DBMS,基于概念数据模型设计了系统相关的逻辑数据模式。对基于关系数据库和NoSQL数据库的解决方案进行了效率实验。为了有效地管理CHD早期识别模型数据,我们提出了考虑建模生命周期的模型版本来表示模型之间的关系。建立模型树,设计查询算法,对冠心病早期识别模型进行谱系管理。基于协同数据管理平台的体系结构设计,为冠心病早期识别和风险预警研究人员实现了有效的患者数据可视化探索服务、队列研究服务以及冠心病早期识别模型选择、模型比较和模型数据可视化探索服务。
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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.
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