医疗大数据搜索系统的建设与应用
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摘要
目的:解决医疗数据多源异构、分布式、碎片化等问题,有效整合分散于医院各独立系统中的数据,更好地为临床科研服务。方法:采用Hadoop分布式的列式数据库、ElasticSearch搜索引擎、Spark集群并行计算框架、R语言等开源大数据技术,结合自然语言处理、机器学习等人工智能技术,依据临床科研需求,建立基于临床数据中心的医疗大数据搜索系统。结果:医疗大数据搜索系统已初具规模,该系统的建设解决了数据杂乱、分散的问题,提高了查询效率和利用率,实现了各系统数据的有效整合。在注重保护患者隐私的同时,支持用户对搜索内容的秒级呈现,可满足组合、多级条件查询及常规查询等需求,取得了良好的应用效果。结论:医疗大数据搜索系统在保障数据安全的前提下,为临床科研提供了便捷、有效的数据查询服务,进一步提高了临床科研数据查询效率及科研服务质量。
Objective To solve the problems of medical data due to multi sources, heterogeneity, distribution and fragmentation so as to integrate data scattered in independent systems. Methods A healthcare big data search system based on clinical data repository was developed with Hadoop database, ElasticSearch search engine, Spark cluster parallel computing frame and R Language in combination with various advanced artificial intelligence(AI) techniques including natural language processing(NLP) and machine learning. Results The system began to take shape, which solved the problems due to data chaos and decentralization, enhanced inquiry efficiency and availability, realized data integration of multi systems, took considerations on patient privacy, executed composite, multi-level and conventional data inquiries and fed back results in several seconds. Conclusion The system developed ensures data safety and implements data inquiry to facilitate clinical scientific research.
Objective To solve the problems of medical data due to multi sources, heterogeneity, distribution and fragmentation so as to integrate data scattered in independent systems. Methods A healthcare big data search system based on clinical data repository was developed with Hadoop database, ElasticSearch search engine, Spark cluster parallel computing frame and R Language in combination with various advanced artificial intelligence(AI) techniques including natural language processing(NLP) and machine learning. Results The system began to take shape, which solved the problems due to data chaos and decentralization, enhanced inquiry efficiency and availability, realized data integration of multi systems, took considerations on patient privacy, executed composite, multi-level and conventional data inquiries and fed back results in several seconds. Conclusion The system developed ensures data safety and implements data inquiry to facilitate clinical scientific research.
引文
[1]李维,计虹.基于Hadoop的医院数据利用探索与实现[J].中国卫生信息管理杂志,2016,13(1):70-74.
[2]席韩旭,李维,计虹.基于临床数据中心的科研平台建设与实践[J].中国数字医学,2017,12(10):8-10.
[3]惠华强,郑萍,张云宏.医疗大数据研究面临的机遇与发展趋势[J].中国卫生质量管理,2016,23(2):91-93.
[4]计虹,李维,贾末.基于大数据的医院集成平台协同与数据融合利用[J].中国卫生信息管理杂志,2017,14(4):525-529.
[5]蔡斌,陈湘萍.Hadoop技术内幕:深入解析Hadoop Common和HDFS架构设计与实现原理[M].北京:机械工业出版社,2013.
[6]郝梅,郑阳,闫华,等.医院医疗设备维护管理系统的设计和应用[J].医疗卫生装备,2016,37(11):54-57.
[7]JAMES M,MICHAL C,BRAD B,et al.Big data:the next frontier for innovation,competition,and productivity[R].Chicago:McKinsey Global Institute,2011.
[8]苏林箐,孙婷婷.健康医疗大数据应用发展与展望[J].无线互联科技,2018(5):143-144.
[2]席韩旭,李维,计虹.基于临床数据中心的科研平台建设与实践[J].中国数字医学,2017,12(10):8-10.
[3]惠华强,郑萍,张云宏.医疗大数据研究面临的机遇与发展趋势[J].中国卫生质量管理,2016,23(2):91-93.
[4]计虹,李维,贾末.基于大数据的医院集成平台协同与数据融合利用[J].中国卫生信息管理杂志,2017,14(4):525-529.
[5]蔡斌,陈湘萍.Hadoop技术内幕:深入解析Hadoop Common和HDFS架构设计与实现原理[M].北京:机械工业出版社,2013.
[6]郝梅,郑阳,闫华,等.医院医疗设备维护管理系统的设计和应用[J].医疗卫生装备,2016,37(11):54-57.
[7]JAMES M,MICHAL C,BRAD B,et al.Big data:the next frontier for innovation,competition,and productivity[R].Chicago:McKinsey Global Institute,2011.
[8]苏林箐,孙婷婷.健康医疗大数据应用发展与展望[J].无线互联科技,2018(5):143-144.