[1]刘莉,姚京京,李俊,等. 基于共词分析和可视化的高血压疾病关联性挖掘[J].中国医学物理学杂志,2019,36(5):614-620.[doi:DOI:10.3969/j.issn.1005-202X.2019.05.024]
　LIU Li,YAO Jingjing,LI Jun,et al. Hypertension related association mining based on co-word analysis and visualization[J].Chinese Journal of Medical Physics,2019,36(5):614-620.[doi:DOI:10.3969/j.issn.1005-202X.2019.05.024]
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 基于共词分析和可视化的高血压疾病关联性挖掘()
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《中国医学物理学杂志》[ISSN:1005-202X/CN:44-1351/R]

卷:
36卷
期数:
2019年第5期
页码:
614-620
栏目:
其他（激光医学等）
出版日期:
2019-05-25

文章信息/Info

Title:
 Hypertension related association mining based on co-word analysis and visualization
文章编号:
1005-202X（2019）05-0614-07
作者:
 刘莉1; 姚京京1; 李俊2; 陈先来3; 周宇葵1
1.中南大学生命科学学院， 湖南 长沙 410013； 2.中南大学湘雅口腔医学院， 湖南 长沙 410008； 3.中南大学信息安全与大数据研究院， 湖南 长沙 410083
Author(s):
LIU Li1;  YAO Jingjing1;  LI Jun2;  CHEN Xianlai3;  ZHOU Yukui1
 1. School of Life Science, Central South University, Changsha 410013, China; 2. Xiangya School of Stomatology, Central South University, Changsha 410008, China; 3. Institute of Information Security and Big Data, Central South University, Changsha 410083, China
关键词:
 ; 电子病历; 病案首页; 高血压; 共词分析; 可视化
Keywords:
 ; Keywords: electronic medical record;  medical record home page;  hypertension;  co-word analysis;  visualization
分类号:
TP391；R312
DOI:
DOI:10.3969/j.issn.1005-202X.2019.05.024
文献标志码:
A
摘要:
 目的：对高血压患者电子病历病案首页进行分析挖掘，揭示其中疾病诊断之间的关系。方法：以共词分析为基础，通过Python语言构建分析模块，采用Gephi复杂网络分析软件对结果进行展示。结果：基于3 632条电子病历记录，构建包含疾病诊断节点1 029个，共现关系边12 479条的疾病诊断共现网络，发现共现关系较强的疾病诊断集群。结论：从多角度、多层面对疾病诊断共现网络进行解读，并以可视化图谱的方式展示，揭示疾病诊断之间关系，为下一步构建更加完善的疾病图谱奠定基础。
Abstract:
Abstract: Objective To analyze and mine the electronic medical record home page of patients with hypertension, and to reveal the relationships among disease diagnoses. Methods Based on the co-word analysis, the analysis module was established by Python language, and the analysis results were displayed by Gephi complex network analysis software. Results Based on 3 362 electronic medical records, a disease diagnosis co-occurrence network containing 1 029 disease diagnosis nodes and 12 479 co-occurrence relationship lines was constructed, and a disease diagnosis cluster with strong co-occurrence relationship was found. Conclusion The disease diagnosis co-occurrence network can be interpreted from multi-angle and multi-level, and can be displayed in a visual map to reveal the relationships among disease diagnoses, laying a foundation for the further establishment of a more complete disease map.

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备注/Memo

备注/Memo:
 【收稿日期】2019-01-19
【基金项目】国家重点研发计划（2016YFC0901705）
【作者简介】刘莉，副教授，E-mail: 332140915@qq.com；姚京京，在读硕士研究生，E-mail: 446124548@qq.com
【通信作者】李俊，讲师，主要研究方向：医院信息系统、计算机网络，E-mail: lijun2016@csu.edu.cn
更新日期/Last Update: 2019-05-23