健康医疗大数据研究进展
|
摘要
健康医疗大数据是指通过电子病历、病案监测、生物数据、公共卫生信息、医保数据等多渠道获取到的医疗数据,通过数据结构化、图像分析、智能检测等技术在临床决策支持、药物研发、疾病监控和健康管理等领域有着广泛应用。健康医疗大数据具有数据体量巨大、增长处理速度快、数据结构多样化和应用价值高等特征,获取、转化及分析大数据的速度和能力成为各国生命经济发展的新引擎。健康医疗大数据是国家重要的基础性战略资源,是以创新推进供给侧结构性改革的重大民生工程。为此,在阐述国内外健康医疗大数据发展现状及其趋势的基础上,展望未来健康医疗大数据的发展特征,提出加强大数据管理的若干建议。
Big data of healthcare and medical treatment refers to the medical data acquires from many channels included electronic medical record, medical record monitoring, biological data, public health information and medical insurance data. It is widely used in the fields of clinical decision support, drug research and development, disease monitoring and control, and health management through data structuration, image analysis, intelligent detection and other techniques. Big data of health care and medical treatment has series of characteristics includes huge volume of data, fast growth and processing speed, diversified data structure and high application value. The speed and ability that acquires, transforms and analyzes big data has become a new engine of life and economic development of various countries. In the meanwhile, big data of healthcare and medical treatment is important strategic resource with fundamentality for a country, and it is also a major livelihood project that promotes structural reform of supply-side through innovation. Therefore, this paper looks forward to the future of the development of the big data of healthcare and medical treatment on the basis of analyzing the state-of-the-art and development tendency of big data of healthcare and medical treatment at home and abroad.
Big data of healthcare and medical treatment refers to the medical data acquires from many channels included electronic medical record, medical record monitoring, biological data, public health information and medical insurance data. It is widely used in the fields of clinical decision support, drug research and development, disease monitoring and control, and health management through data structuration, image analysis, intelligent detection and other techniques. Big data of health care and medical treatment has series of characteristics includes huge volume of data, fast growth and processing speed, diversified data structure and high application value. The speed and ability that acquires, transforms and analyzes big data has become a new engine of life and economic development of various countries. In the meanwhile, big data of healthcare and medical treatment is important strategic resource with fundamentality for a country, and it is also a major livelihood project that promotes structural reform of supply-side through innovation. Therefore, this paper looks forward to the future of the development of the big data of healthcare and medical treatment on the basis of analyzing the state-of-the-art and development tendency of big data of healthcare and medical treatment at home and abroad.
引文
[1]蔡佳慧,张涛,宗文红.医疗大数据面临的挑战及思考[J].中国卫生信息管理杂志,2013(4):292-295.
[2]代涛.健康医疗大数据发展应用的思考[J].医学信息学杂志,2016,37(2):2-8.
[3]马家奇.公共卫生大数据应用[J].中国卫生信息管理杂志,2014(2):174-177.
[4]Obermeyer Z,Emanuel EJ.Predicting the future-big data,machine learning,and clinical medicine[J].N Engl J Med,2016,375(13):1216-1219.
[5]Murdoch TB,Detsky AS.The inevitable application of big data to health care[J].JAMA,2013,309(13):1351-1352.
[6]Alyass A,Turcotte M,Meyre D.From big data analysis to personalized medicine for all:challenges and opportunities[J].BMC Med Genomics,2015,8:33.
[7]Archenaa J,Anita EAM.A survey of big data analytics in healthcare and government[J].Procedia Computer Science,2015,50:408-413.
[8]刘旷.大数据催化医疗业务边界[J].现代企业文化,2018(19):66-69.
[9]颜延,秦兴彬,樊建平,等.医疗健康大数据研究综述[J].科研信息化技术与应用,2014,5(6):3-16.
[10]Raghupathi W,Raghupathi V.Big data analytics in healthcare:promise and potential[J].Health Inf Sci Syst,2014,2:3.
[11]Chen Y,Argentinis JDE,Weber G.IBMWatson:how cognitive computing can be applied to big data challenges in life sciences research[J].Clin Ther,2016,38(4):688-701.
[12]Rose J,Burgin M.Disrupting health care through big data and predictive analytics[J].Managed Care Outlook,2014,27(1):11.
[13]Bibault JE,Giraud P,Burgun A.Big data and machine learning in radiation oncology:state of the art and future prospects[J].Cancer Lett,2016,382(1):110-117.
[14]Mombers C,Legako K,Gilchrist A.Identifying medical wearables and sensor technologies that deliver data on clinical endpoints[J].Br J Clin Pharmacol,2016,81(2):196-198.
[15]Tarvainen MP,Niskanen JP,Lipponen JA,et al.Kubios HRV-heart rate variability analysis software[J].Comput Methods Programs Biomed,2014,113(1):210-220.
[16]Hashem IAT,YaqoobI,Anuar NB,et al.The rise of"big data"on cloud computing:Review and open research issues[J].Information Systems,2015,47:98-115.
[17]刘伟,倪桑.2017年人工智能研发热点回眸[J].科技导报,2018,36(1):98-103.
[18]吴娜,许利群.移动医疗产业发展机遇和挑战探究[J].互联网天地,2015,12(8):6-12.
[19]周光华,辛英,张雅洁,等.医疗卫生领域大数据应用探讨[J].中国卫生信息管理杂志,2013(4):296-300.
[20]宁康,陈挺.生物医学大数据的现状与展望[J].科学通报(中文版),2015,60(5/6):534-546.
[21]郭庆.“BAT全覆盖”智慧医院的探索与实践[J].海南医学,2015(16):48.
[22]Sun J,Reddy CK.Big data analytics for healthcare[C].Chicago:Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining,2013:1525.
[23]翟曦,周莲茹,焦雄飞.医院信息化的大数据应用进展[J].中国医学装备,2018,15(7):146-149.
[2]代涛.健康医疗大数据发展应用的思考[J].医学信息学杂志,2016,37(2):2-8.
[3]马家奇.公共卫生大数据应用[J].中国卫生信息管理杂志,2014(2):174-177.
[4]Obermeyer Z,Emanuel EJ.Predicting the future-big data,machine learning,and clinical medicine[J].N Engl J Med,2016,375(13):1216-1219.
[5]Murdoch TB,Detsky AS.The inevitable application of big data to health care[J].JAMA,2013,309(13):1351-1352.
[6]Alyass A,Turcotte M,Meyre D.From big data analysis to personalized medicine for all:challenges and opportunities[J].BMC Med Genomics,2015,8:33.
[7]Archenaa J,Anita EAM.A survey of big data analytics in healthcare and government[J].Procedia Computer Science,2015,50:408-413.
[8]刘旷.大数据催化医疗业务边界[J].现代企业文化,2018(19):66-69.
[9]颜延,秦兴彬,樊建平,等.医疗健康大数据研究综述[J].科研信息化技术与应用,2014,5(6):3-16.
[10]Raghupathi W,Raghupathi V.Big data analytics in healthcare:promise and potential[J].Health Inf Sci Syst,2014,2:3.
[11]Chen Y,Argentinis JDE,Weber G.IBMWatson:how cognitive computing can be applied to big data challenges in life sciences research[J].Clin Ther,2016,38(4):688-701.
[12]Rose J,Burgin M.Disrupting health care through big data and predictive analytics[J].Managed Care Outlook,2014,27(1):11.
[13]Bibault JE,Giraud P,Burgun A.Big data and machine learning in radiation oncology:state of the art and future prospects[J].Cancer Lett,2016,382(1):110-117.
[14]Mombers C,Legako K,Gilchrist A.Identifying medical wearables and sensor technologies that deliver data on clinical endpoints[J].Br J Clin Pharmacol,2016,81(2):196-198.
[15]Tarvainen MP,Niskanen JP,Lipponen JA,et al.Kubios HRV-heart rate variability analysis software[J].Comput Methods Programs Biomed,2014,113(1):210-220.
[16]Hashem IAT,YaqoobI,Anuar NB,et al.The rise of"big data"on cloud computing:Review and open research issues[J].Information Systems,2015,47:98-115.
[17]刘伟,倪桑.2017年人工智能研发热点回眸[J].科技导报,2018,36(1):98-103.
[18]吴娜,许利群.移动医疗产业发展机遇和挑战探究[J].互联网天地,2015,12(8):6-12.
[19]周光华,辛英,张雅洁,等.医疗卫生领域大数据应用探讨[J].中国卫生信息管理杂志,2013(4):296-300.
[20]宁康,陈挺.生物医学大数据的现状与展望[J].科学通报(中文版),2015,60(5/6):534-546.
[21]郭庆.“BAT全覆盖”智慧医院的探索与实践[J].海南医学,2015(16):48.
[22]Sun J,Reddy CK.Big data analytics for healthcare[C].Chicago:Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining,2013:1525.
[23]翟曦,周莲茹,焦雄飞.医院信息化的大数据应用进展[J].中国医学装备,2018,15(7):146-149.