期刊文献+

移动互联网环境医务人员知识获取行为分析系统设计 被引量:2

Design of Analysis System for Healthcare Professionals′Knowledge Acquisition in Mobile Internet Environment
下载PDF
导出
摘要 移动智能设备在医护人员的工作场景中广泛应用,有助于医务人员随时随地获取专业知识辅助临床决策,提高医疗质量,理解医务人员知识获取行为,可以精准、有效地为医务人员提供知识服务。移动互联网环境构建面向医务人员的知识获取行为分析系统,将医务人员知识获取行为分为静态信息和动态信息两大类,并对每一类进行详细定义,形成医务人员所特有行为标签架构,为医学知识的个性化推荐奠定基础,进而提升医务人员知识获取能力。 With the wide application of smart mobile devices in healthcare working environment, medical professionals can acquire knowledge anytime and anywhere, aiming to assist their clinical decision makings and improve healthcare quality. Therefore, it is important to understand hea]thcare professionals' knowledge acquisition behavior for precise and efficient knowledge service. This study present a behavior analysis system for healthcare professionals' knowledge acquisition in the mobile Internet environment. The behaviors were categorized as static information and dynamic information, which were applied to engrave the professionals' profiles. This system composed of the personalized recommendation of medical educational knowledge, furthelanore, it contributed to the improvement ofhealthcare professionals' knowledge acquisition abilities.
作者 覃露 徐晓巍 李姣 QIN Lu;XU Xiao-wei;LI Jiao(Institute of Medical Information,Chinese Academy of Medical Sciences/Peking Union Medical College,Beijing 100020,P.R.C.)
出处 《中国数字医学》 2018年第9期60-62,共3页 China Digital Medicine
基金 中国工程院医药卫生知识服务系统(编号:CKCEST-2018-1-16) 医学融合出版知识技术重点实验室项目资助~~
关键词 移动互联网 医务人员 行为分析 知识获取 mobile Intemet medical personnel behavior analysis knowledge acquisition
  • 相关文献

参考文献2

二级参考文献44

  • 1沙勇忠,阎劲松,苏云.网络环境下科研人员的信息行为分析[J].情报科学,2006,24(4):485-491. 被引量:37
  • 2任立肖.基于Web日志的三大类型图书馆用户信息行为比较研究[J].图书情报知识,2006,23(6):28-32. 被引量:12
  • 3ZICARI R V. Big data: challenges and opportunities [EB/OL]. [2016-01-08]. http://gotocon.com/dl/goto-aar-2012/slides/RobertoV.Zicari_BigDataChallengesAndOpportunities.pdf.
  • 4ZHAO Q, XIONG C, ZHAO X, et al. A data placement strategy for data-intensive scientific workflows in cloud [C]// Proceedings of the 2015 15th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing. Washington, DC: IEEE Computer Society, 2015: 928-934.
  • 5YU B, PAN J. Location-aware associated data placement for geo-distributed data-intensive applications [C]// Proceedings of the 2015 IEEE Conference on Computer Communications. Piscataway, NJ: IEEE, 2015:603-611.
  • 6JALAPARTI V, BODIK P, MENACHE I, et al. Network-aware scheduling for data-parallel jobs: plan when you can [C]// SIGCOMM '15: Proceedings of the 2015 ACM Conference on Special Interest Group on Data Communication. New York: ACM, 2015: 407-420.
  • 7CHEN W, PAIK I, LI Z. Topology-aware optimal data placement algorithm for network traffic optimization [J]. IEEE Transactions on Computers, 2016,65(8):2603-2617.
  • 8WANG J, QIU M, GUO B, et al. Phase-reconfigurable shuffle optimization for Hadoop MapReduce [J]. IEEE Transactions on Cloud Computing, 2015(99):1.
  • 9YU W, WANG Y, QUE X, et al. Virtual shuffling for efficient data movement in MapReduce [J]. IEEE Transactions on Computers, 2015, 64(2):556-568.
  • 10BUYYA R. High Performance Cluster Computing: Architectures and Systems [M]. Upper Saddle River, NJ: Prentice Hall, 1999, 1: 823.

共引文献36

同被引文献14

引证文献2

二级引证文献8

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部