@Article{信息:doi 10.2196 / / jmir。1636，作者=“Doing-Harris, Kristina M and Zeng-Treitler, Qing”，标题=“基于社交网络数据挖掘的消费者健康词汇的计算机辅助更新”，期刊=“J Med Internet Res”，年=“2011”，月=“5”，日=“17”，卷=“13”，数=“2”，页数=“e37”，关键词=“消费者健康信息;词汇表;自然语言处理;词语自动识别;数据挖掘;背景:消费者健康词汇表(CHVs)已被开发用于帮助消费者健康信息学应用。如果词汇表随着消费者语言的变化而变化，就能最好地达到这个目的。目的:我们的目标是创建一个计算机辅助更新(CAU)系统，该系统与实时语料库一起工作，以确定新的候选术语，以纳入开放获取与协作(OAC) CHV。方法:CAU系统由三个主要部分组成:Web爬虫和HTML解析器，利用自然语言处理工具(包括术语识别方法)的候选术语过滤器，以及人工审阅界面。 In evaluation, the CAU system was applied to the health-related social network website PatientsLikeMe.com. The system's utility was assessed by comparing the candidate term list it generated to a list of valid terms hand extracted from the text of the crawled webpages. Results: The CAU system identified 88,994 unique terms 1- to 7-grams (``n-grams'' are n consecutive words within a sentence) in 300 crawled PatientsLikeMe.com webpages. The manual review of the crawled webpages identified 651 valid terms not yet included in the OAC CHV or the Unified Medical Language System (UMLS) Metathesaurus, a collection of vocabularies amalgamated to form an ontology of medical terms, (ie, 1 valid term per 136.7 candidate n-grams). The term filter selected 774 candidate terms, of which 237 were valid terms, that is, 1 valid term among every 3 or 4 candidates reviewed. Conclusion: The CAU system is effective for generating a list of candidate terms for human review during CHV development. ", issn="1438-8871", doi="10.2196/jmir.1636", url="//www.mybigtv.com/2011/2/e37/", url="https://doi.org/10.2196/jmir.1636", url="http://www.ncbi.nlm.nih.gov/pubmed/21586386" }