{"title":"大数据在生物医学研究和医疗保健中的应用:文献综述。","authors":"Jake Luo, Min Wu, Deepika Gopukumar, Yiqing Zhao","doi":"10.4137/BII.S31559","DOIUrl":null,"url":null,"abstract":"<p><p>Big data technologies are increasingly used for biomedical and health-care informatics research. Large amounts of biological and clinical data have been generated and collected at an unprecedented speed and scale. For example, the new generation of sequencing technologies enables the processing of billions of DNA sequence data per day, and the application of electronic health records (EHRs) is documenting large amounts of patient data. The cost of acquiring and analyzing biomedical data is expected to decrease dramatically with the help of technology upgrades, such as the emergence of new sequencing machines, the development of novel hardware and software for parallel computing, and the extensive expansion of EHRs. Big data applications present new opportunities to discover new knowledge and create novel methods to improve the quality of health care. The application of big data in health care is a fast-growing field, with many new discoveries and methodologies published in the last five years. In this paper, we review and discuss big data application in four major biomedical subdisciplines: (1) bioinformatics, (2) clinical informatics, (3) imaging informatics, and (4) public health informatics. Specifically, in bioinformatics, high-throughput experiments facilitate the research of new genome-wide association studies of diseases, and with clinical informatics, the clinical field benefits from the vast amount of collected patient data for making intelligent decisions. Imaging informatics is now more rapidly integrated with cloud platforms to share medical image data and workflows, and public health informatics leverages big data techniques for predicting and monitoring infectious disease outbreaks, such as Ebola. In this paper, we review the recent progress and breakthroughs of big data applications in these health-care domains and summarize the challenges, gaps, and opportunities to improve and advance big data applications in health care. </p>","PeriodicalId":88397,"journal":{"name":"Biomedical informatics insights","volume":"8 1","pages":"1-10"},"PeriodicalIF":0.0000,"publicationDate":"2016-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4720168/pdf/","citationCount":"0","resultStr":"{\"title\":\"Big Data Application in Biomedical Research and Health Care: A Literature Review.\",\"authors\":\"Jake Luo, Min Wu, Deepika Gopukumar, Yiqing Zhao\",\"doi\":\"10.4137/BII.S31559\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>Big data technologies are increasingly used for biomedical and health-care informatics research. 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引用次数: 0
摘要
大数据技术越来越多地用于生物医学和保健信息学研究。大量生物和临床数据以前所未有的速度和规模产生和收集。例如,新一代测序技术每天可处理数十亿 DNA 序列数据,电子健康记录(EHR)的应用记录了大量患者数据。在技术升级的帮助下,获取和分析生物医学数据的成本有望大幅降低,如新型测序机的出现、用于并行计算的新型硬件和软件的开发以及电子病历的广泛扩展。大数据应用为发现新知识、创造新方法以提高医疗质量提供了新机遇。大数据在医疗保健中的应用是一个快速发展的领域,在过去的五年中发表了许多新的发现和方法。本文回顾并讨论了大数据在四大生物医学分支学科中的应用:(1) 生物信息学;(2) 临床信息学;(3) 影像信息学;以及 (4) 公共卫生信息学。具体来说,在生物信息学方面,高通量实验促进了新的疾病全基因组关联研究;在临床信息学方面,临床领域从收集的大量病人数据中获益,从而做出智能决策。目前,影像信息学与云平台的整合更加迅速,可共享医学影像数据和工作流程;公共卫生信息学则利用大数据技术预测和监测埃博拉等传染病的爆发。在本文中,我们回顾了大数据应用在这些医疗保健领域的最新进展和突破,并总结了改善和推进医疗保健领域大数据应用所面临的挑战、差距和机遇。
Big Data Application in Biomedical Research and Health Care: A Literature Review.
Big data technologies are increasingly used for biomedical and health-care informatics research. Large amounts of biological and clinical data have been generated and collected at an unprecedented speed and scale. For example, the new generation of sequencing technologies enables the processing of billions of DNA sequence data per day, and the application of electronic health records (EHRs) is documenting large amounts of patient data. The cost of acquiring and analyzing biomedical data is expected to decrease dramatically with the help of technology upgrades, such as the emergence of new sequencing machines, the development of novel hardware and software for parallel computing, and the extensive expansion of EHRs. Big data applications present new opportunities to discover new knowledge and create novel methods to improve the quality of health care. The application of big data in health care is a fast-growing field, with many new discoveries and methodologies published in the last five years. In this paper, we review and discuss big data application in four major biomedical subdisciplines: (1) bioinformatics, (2) clinical informatics, (3) imaging informatics, and (4) public health informatics. Specifically, in bioinformatics, high-throughput experiments facilitate the research of new genome-wide association studies of diseases, and with clinical informatics, the clinical field benefits from the vast amount of collected patient data for making intelligent decisions. Imaging informatics is now more rapidly integrated with cloud platforms to share medical image data and workflows, and public health informatics leverages big data techniques for predicting and monitoring infectious disease outbreaks, such as Ebola. In this paper, we review the recent progress and breakthroughs of big data applications in these health-care domains and summarize the challenges, gaps, and opportunities to improve and advance big data applications in health care.