期刊文献+

基于Spark大数据平台与改进Adaboost算法的医院预分检系统研究

Research on hospital pre-triage system based on Spark big data platform and improved Adaboost algorithm
在线阅读 下载PDF
导出
摘要 目的:设计基于Spark大数据平台与改进Adaboost算法的医院预分检系统,用于医院就诊患者诊前分流,加速患者就医流程。方法:基于Spark大数据平台实时采集初次进入医院就诊患者的基础数据,将区块链技术应用于数据采集、存储与传输全过程,通过改进Adaboost算法对数据进行分析,采用2011—2020年联勤保障部队第九四〇医院10年间门诊患者的就诊数据为数据集,对患者在院内就诊进行快速甄别并引导就诊。分析基于Spark大数据平台与改进Adaboost算法的医院预分检系统应用效果。结果:改进Adaboost算法设置自定义限制权重阈值为0.52时,算法准确率为95.56%,预检分诊准确率较传统Adaboost算法提高4.24%。患者平均候诊时间由采用预分检系统前的0.8 h缩短为0.5 h,患者平均就诊时间由6 min缩短为4.8 min。结论:基于大数据平台与改进Adaboost算法的医院预分检系统能够提前将医院就诊患者进行诊前分流,提高分检效率和分检准确率,缓解医院就诊压力。 Objective:To design a hospital pre-triage system based on the Spark big data platform and the improved Adaboost algorithm,and to pre-triage patients in the hospital in advance and accelerate the process of medical treatment.Methods:Based on the Spark big data platform,the basic data from patients entering the hospital for the first time was collected in real time,and the blockchain technology was applied to the whole process of data collection,storage and transmission,and the data was analyzed by the improved the Adaboost algorithm.The outpatient data of The 940th Hospital of the PLA Joint Logistics Support Force in the 10 years from 2011 to 2020 were used as the dataset to quickly identify and guide patients to seek medical treatment in the hospital.The application effect of the hospital pre-triage system based on the Spark big data platform and the improved Adaboost algorithm was analyzed.Results:When the custom limit weight threshold of the improved Adaboost algorithm was set to 0.52,the algorithm accuracy reached a peak of 95.56%,and the accuracy of pre-test triage was 4.24%higher than that of the traditional Adaboost algorithm.The average waiting time for patients was shortened from 0.8 h before the triage to 0.5 h,and the average consultation time for patients was shortened from 6 min before the triage to 4.8 min.Conclusion:The hospital pre-triage system based on the big data platform and the improved Adaboost algorithm can pre-triage patients before diagnosis in advance,improve the efficiency and accuracy of the triage,and relieve the hospital visiting pressure.
作者 李宗仁 陈辉 常俊 王能才 Li Zongren;Chen Hui;Chang Jun;Wang Nengcai(Information Division,The 940th Hospital,PLA Joint Logistics Support Force,Lanzhou 730050 China;Sanitary Economics Department,The 940th Hospital,PLA Joint Logistics Support Force,Lanzhou 730050 China)
出处 《中国医学装备》 2024年第9期102-106,共5页 China Medical Equipment
基金 甘肃省青年科技基金计划(22JR11RA004)。
关键词 预分检 实时采集 Spark大数据平台 改进Adaboost算法 Pre-triage Real-time collection Spark big data platform Improved Adaboost algorithm
作者简介 通信作者:陈辉,Email:chenhui7409@163.com。
  • 相关文献

参考文献16

二级参考文献155

  • 1蔡莉,王淑婷,刘俊晖,朱扬勇.数据标注研究综述[J].软件学报,2020,31(2):302-320. 被引量:72
  • 2程蔼隽,戚海,左贵峰,申悦霞.重大疾病区域联防联控[J].预防医学情报杂志,2007,23(1):90-92. 被引量:9
  • 3GuoG D, Zhang H J. Boosting for Fast Face Recognition. In: Proc of 2nd International Workshop on Recognition, Analysis and Tracking of Faces and Gestures in Real-Time Systems. Vancouver, Canada, 2001, 96- 100.
  • 4Abney S, Schapire R E, Singer Y. Boosting Applied to Tagging and PP Attachment. ln: Proc of the Joint SIGDAT Conference on Empirical Methods in Natural Language Processing and Very Large Corpora. New Brunswick, NJ, 1999, 38-45.
  • 5Rochery M, Schapire R E, Rahim M, Gupta N. BoosTexter for Text Categorization in Spoken Language Dialogue. In: Autmmtic Speech Recognition and Understanding Workshop. Madonna di Campiglio Trento, Italy, 2001. Available at http://www, cs.princeton, edu/-schapire/publist, html.
  • 6Rochery M, Schapire R, Rahim M, Gupta N, Riceardi G, Bangalore S, Alshawi H, Douglas S. Combining Prior Knowledge and Boosting for Call Class~flcat~on in Spoken Language DiaLogue. In:Proc of International Conference on Aceousties, Speech and Signal. Orlando, Florida. 2002. Available at http://www, cs/princetonedu/-schapire/whatsnew. html.
  • 7Schapire R E, Singer Y. BcosTexter: A Bcosting-Based System for Text Categorization. Machine Learning, 2000, 39(2- 3): 135- 168.
  • 8Schapire R E, Rochery M, Rahim M, Gupta N. Incorporating Prior Knowledge into Boosting. In: Proc of the 19th International Conference on Machine Learning. Sydney, 2002, 538 - 545.
  • 9Schwenk H, Bengio Y. Adal3oosting Neural Networks: Application to On-Line Character Recognition. In: Proc of the International Conference on Artificial Neural Networks ( ICANN' 97 ). Lausanne, Switzerland: Springer-Verlag, 1997, 967-972.
  • 10Schwenk H. Using Boosting to lmprove a Hybrid HMM/'Neural Network Speech Recognizer. In: Proc of the IEEE International Conferenee on Acoustics, Speech, and Signal (ICASSP 99 ). Phoenix, Arizona, 1999, H : 1009 - 1012.

共引文献193

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