Design and Development of a Medical Big Data Processing System Based on Hadoop.
Secondary use of medical big data is increasingly popular in healthcare services and clinical research. Understanding the logic behind medical big data demonstrates tendencies in hospital information technology and shows great significance for hospital information systems that are designing and expa...
| Publicado en: | Journal of Medical Systems Vol. 39; no. 3; pp. 1 - 12 |
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| Autores principales: | , , , , , |
| Formato: | algorithm case study equations & formulas research tables/charts Journal Article |
| Publicado: |
Springer Nature
Mar2015
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=115925405&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 115925405 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01485598 4N0 jtl: Journal of Medical Systems issn: 01485598 maglogo: N pubinfo: dt: Mar2015 vid: 39 iid: 3 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 115925405 115925405 115925405 10.1007/s10916-015-0220-8 115925405 ppf: 1 ppct: 11 formats: fmt: @attributes: type: P tig: atl: Design and Development of a Medical Big Data Processing System Based on Hadoop. aug: au: Yao, Qin Tian, Yu Li, Peng-Fei Tian, Li-Li Qian, Yang-Ming Li, Jing-Song affil: Engineering Research Center of EMR and Intelligent Expert System, Ministry of Education, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou China sug: subj: Data Analytics Systems Development Hospital Information Systems Cloud Computing Funding Source Algorithms Quantitative Studies Human User-Computer Interface Case Studies Hospitals China Comparative Studies Data Warehouse ab: Secondary use of medical big data is increasingly popular in healthcare services and clinical research. Understanding the logic behind medical big data demonstrates tendencies in hospital information technology and shows great significance for hospital information systems that are designing and expanding services. Big data has four characteristics - Volume, Variety, Velocity and Value (the 4 Vs) - that make traditional systems incapable of processing these data using standalones. Apache Hadoop MapReduce is a promising software framework for developing applications that process vast amounts of data in parallel with large clusters of commodity hardware in a reliable, fault-tolerant manner. With the Hadoop framework and MapReduce application program interface (API), we can more easily develop our own MapReduce applications to run on a Hadoop framework that can scale up from a single node to thousands of machines. This paper investigates a practical case of a Hadoop-based medical big data processing system. We developed this system to intelligently process medical big data and uncover some features of hospital information system user behaviors. This paper studies user behaviors regarding various data produced by different hospital information systems for daily work. In this paper, we also built a five-node Hadoop cluster to execute distributed MapReduce algorithms. Our distributed algorithms show promise in facilitating efficient data processing with medical big data in healthcare services and clinical research compared with single nodes. Additionally, with medical big data analytics, we can design our hospital information systems to be much more intelligent and easier to use by making personalized recommendations. pubtype: Academic Journal doctype: algorithm case study equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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