基于医院自助设备日志的用户行为学研究
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摘要
目的:通过自助设备日志分析,提升自助机用户体验和医院自助设备管理水平。方法:对设备日志正则解析后,利用Python的pandas、numpy、scipy、matplotlib库进行数据预处理和挖掘分析。结果:对设备、用户行为以及用户需求分别进行画像,并提出一种对可疑用户行为的监测方式。结论:通过数据分析,可提高医疗资源及时决策调拨能力及医院自助设备管理水平。
Obejctive: Improve self-service user experience and hospital self-service equipment management level through selfservice device log analysis. Methods: After parsing the device log, Python's pandas, numpy, scipy, and matplotlib libraries were used for data preprocessing and mining analysis, after parsing the device log through regular expression. Results: The equipment, user behavior and user needs were respectively imaged, and a way to monitor the behavior of suspicious users was proposed. Conclusion: Data analysis can improve the timely decision-making capacity of medical resources and the level of hospital self-service equipment management.
Obejctive: Improve self-service user experience and hospital self-service equipment management level through selfservice device log analysis. Methods: After parsing the device log, Python's pandas, numpy, scipy, and matplotlib libraries were used for data preprocessing and mining analysis, after parsing the device log through regular expression. Results: The equipment, user behavior and user needs were respectively imaged, and a way to monitor the behavior of suspicious users was proposed. Conclusion: Data analysis can improve the timely decision-making capacity of medical resources and the level of hospital self-service equipment management.
引文
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[2]中共中央国务院印发《"健康中国2030"规划纲要》[J].中华人民共和国国务院公报,2016(32):6-21.
[3]十九大报告明确建设健康中国路线图[J].医学信息学杂志,2017(10):94.
[4]张国明,陈安琪.基于区域健康信息平台的医疗大数据利用探索[J].中国卫生信息管理杂志,2016,13(3):290-294.
[5]Mckinney W.利用Python进行数据分析[J].徐敬一,译.北京:机械工业出版社,2018:7-13.
[6]Wu S.A review on coarse warranty data and analysis[J].Reliability Engineering and System,2013(114):1-11.
[7]Wu X,Kumar V,Quinlan JR,et al.Top 10algorithms in data mining[J].Knowledge and information systems,2008,14(1):1-37.
[8]Kanungo T,Mount DM,Netanyahu NS,et al.An efficient k-means clustering algorithm:Analysis and implementation[J].IEEETransactions on Pattern Analysis and Machine Intelligence,2002,24(7):881-892.
[9]邓玉洁,朱庆生.基于聚类的离群点分析方法[J].计算机应用研究,2012,29(3):865-868.