摘要
目的了解在线医疗社区用户对脑卒中疾病信息的需求,为开展符合脑卒中患者需求的医疗护理服务奠定基础。方法采用数据挖掘的方法,研究对象为3个中文在线医疗社区中与脑卒中有关的提问记录,通过爬虫代码采集2020年8月1日—2021年7月31日的数据,进行数据清洗和分词后采用潜在狄利克雷分配(LDA)主题模型挖掘主题特征。结果在线医疗社区提问记录的主题特征分析显示,LDA模型将33731条提问记录划分4个方面和8个主题的信息需求,分别为症状的识别和应急处理(症状、突发症状和应急措施)、医疗诊治(检查和治疗、医疗诊断、手术)、康复保健(起居照顾、饮食和药物)、患者和照顾者心理护理知识需求(患者或照顾者对疾病预后的担心)。结论基于LDA主题模型的研究结果可反映脑卒中用户关于在线医疗社区的信息需求,为脑卒中专病护理信息需求及其结构化健康教育的开展提供了发展方向和信息支撑。
Objective To understand the needs of online medical community users for stroke disease information,and lay the foundation for providing medical care services that meet the needs of stroke patients.Methods Data mining was used to examine the question records related to stroke in 3 Chinese online medical communities.The data from August 1,2020 to July 31,2021 were collected through the crawler code,and the themes were mined using the Latent Dirichlet Allocation(LDA)model after data cleaning and word segmentation.Results The subject feature analysis of the online medical community question records showed that the LDA model divided 33731 question records into 4 aspects and 8 themes of information needs,namely,symptom identification and emergency treatment(symptoms,sudden symptoms and emergency measures),medical diagnosis and treatment(examination and treatment,medical diagnosis,surgery),rehabilitation care(daily care,diet and medication),and patients and caregivers'needs for psychological care knowledge(patients or caregivers'concerns about disease prognosis).Conclusions The research results based on LDA model can reflect the information needs of online medical community users with stroke,and provide the development direction and information support for meeting the information needs of stroke specialist care and providing structured health education.
作者
何旭东
杨方燕
段红梅
He Xudong;Yang Fangyan;Duan Hongmei(School of Nursing,Beijing University of Chinese Medicine,Beijing 100102,China)
出处
《中华现代护理杂志》
2022年第16期2126-2130,共5页
Chinese Journal of Modern Nursing
基金
中华医学会杂志社2021-2022年护理学科研究课题(CMAPH-NRI2021062)。
关键词
脑卒中
健康教育
在线医疗社区
LDA模型
Stroke
Health education
Online medical community
Latent Dirichlet Allocation model