Enriching consumer health vocabulary through mining a social Q&A site: A similarity-based approach.
Enriching consumer health vocabulary through mining a social Q&A site: A similarity-based approach.
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DOI：
10.1016/j.jbi.2017.03.016
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发表时间：
2017-05
影响因子：
4.5
通讯作者：
Bian J
中科院分区：
文献类型：
--
作者：
He Z;Chen Z;Oh S;Hou J;Bian J
众所周知，健康消费者和医疗保健专业人员之间的词汇差距阻碍了消费者在最终用户健康应用程序上寻求信息和健康对话。开放获取和合作消费者健康词汇(OAC CHV)包含了非专业消费者使用的与健康相关的术语，它的创建就是为了弥合这一差距。具体地说，OAC CHV通过使面向消费者的健康应用程序能够在专业语言和消费者友好语言之间进行转换，促进了消费者的健康信息检索。为了跟上不断发展的医学知识和语言使用，需要识别新的术语并将其添加到OAC CHV中。社交媒体上的用户生成内容，包括社交问答网站，为我们提供了挖掘消费者健康术语的巨大机会。现有的从文本中识别新消费者术语的方法通常使用自组织词汇句法模式和人工审查。我们的研究扩展了现有的方法，从社会问答文本语料库中提取n-gram，并用一组丰富的上下文和句法特征来表示它们。使用K-Means聚类，我们的方法simiTerm能够识别在上下文和语法上与现有OAC CHV术语相似的术语。我们在社会问答语料库上测试了我们的方法，涉及两个疾病领域：糖尿病和癌症。我们的方法比三种基线排名方法都要好。人类专家的事后定性评估进一步验证了我们的方法可以有效地识别出关于社会问答的有意义的新消费术语。
The widely known vocabulary gap between health consumers and healthcare professionals hinders information seeking and health dialogue of consumers on end-user health applications. The Open Access and Collaborative Consumer Health Vocabulary (OAC CHV), which contains health-related terms used by lay consumers, has been created to bridge such a gap. Specifically, the OAC CHV facilitates consumers’ health information retrieval by enabling consumer-facing health applications to translate between professional language and consumer friendly language. To keep up with the constantly evolving medical knowledge and language use, new terms need to be identified and added to the OAC CHV. User-generated content on social media, including social question and answer (social Q&A) sites, afford us an enormous opportunity in mining consumer health terms. Existing methods of identifying new consumer terms from text typically use ad-hoc lexical syntactic patterns and human review. Our study extends an existing method by extracting n-grams from a social Q&A textual corpus and representing them with a rich set of contextual and syntactic features. Using K-means clustering, our method, simiTerm, was able to identify terms that are both contextually and syntactically similar to the existing OAC CHV terms. We tested our method on social Q&A corpora on two disease domains: diabetes and cancer. Our method outperformed three baseline ranking methods. A post-hoc qualitative evaluation by human experts further validated that our method can effectively identify meaningful new consumer terms on social Q&A.