基于文本挖掘的互联网医疗平台用户画像模型构建
Construction of the User Portrait Model of Internet Medical Platform Based on Text Mining
吕艳华 1王康龙 1钟小云 1陈俊冶1
作者信息
- 1. 山西医科大学管理学院 太原 030600
- 折叠
摘要
目的/意义 构建互联网问诊用户画像探究问诊主题,提升问诊服务质量,减少医患沟通障碍,以线上配合线下方式针对性治疗.方法/过程 利用Python爬虫获取好大夫在线医疗平台孤独症疾病问诊数据,运用隐含狄利克雷分布(latent Dirichlet allocation,LDA)与词频-逆文档频率(term frequency-inverse doc-ument frequency,TF-IDF)结合的模型划分数据,在降维聚类后实现用户群体分类.最后通过logistic回归模型计算输出不同用户群体特征集合,构建画像.结果/结论 用户问诊内容主要围绕11个主题展开,平台可通过主题内容的典型特征优化问诊填写模板,提高用户填写疾病描述准确性、问诊效率和患者满意度.
Abstract
Purpose/Significance The internet consultation user portrait is constructed to explore the consultation topic,improve the consultation service quality,reduce the communication barriers between doctors and patients,and provide targeted treatment in an online and offline manner.Method/Process Python crawler is used to obtain the autism diagnosis data of a medical platform,and the combined model of LDA and TF-TFIDF is used to divide the data,and the user group classification is realized after dimensionality reduction clus-tering.Finally,the characteristic sets of different user groups are calculated and output by logistic regression model to construct the por-trait.Result/Conclusion The consultation content of users mainly focuses on 11 topics.The platform can optimize the consultation filling template based on the typical characteristics of the subject content to improve the accuracy of the disease description,consultation effi-ciency and satisfaction of patients.
关键词
互联网医疗平台/主题模型/文本分析/用户画像Key words
internet medical platform/topic model/text analysis/user portrait引用本文复制引用
基金项目
国家社会科学基金一般项目(20BTQ064)
出版年
2024
NETL
NSTL
万方数据