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A study on health care consumers' diabetes term usage across identified categories

机译:一项针对已确定类别的医疗保健消费者糖尿病术语使用情况的研究

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Purpose - The purpose of this paper is to investigate health care consumers' diabetes term usage patterns based on YahoolAnswers social question and answers (Q&A) forum, identified characteristics and relationships among terms within three pairs of related categories identified from the Q&A log, and revealed users' diabetes term usage patterns. Design/methodology/approach - The Q&A analysis method allowed first-hand investigation of massive data from health care consumers. Visual term clustering analysis across categories was conducted using a multi-dimensional scaling (MDS) visualization method which provides an intuitive and interactive way to explore and discover term association patterns in a visual environment. Closely related categories were identified and corresponding visual term clustering analyses between categories (Sign & Symptom and Organ & Body Part; Diagnosis and Test; and Diagnosis and Medication) as well at the term level were analyzed. Findings - The findings show that there are close relationships between terms in two related categories. Related terms were grouped and patterns were revealed. All the stress values of the MDS analyses fall below 0.10 and RSQ for each of the combined categories is over 0.90 which indicate the investigated terms were well clustered in the visual analyses. Originality/value - The study provides a unique research methodology for similar consumer health research studies. The results of this study offer insight into consumer health term use behavior, and enrich existing thesauri and subject heading lists, enhance diabetes-related web sites or portals, and improve effectiveness of internal search engines.
机译:目的-本文的目的是根据YahoolAnswers社会问答网站(Q&A)调查医疗保健消费者的糖尿病术语使用模式,从Q&A日志中识别出三对相关类别中的术语之间的特征和关系,并进行揭示用户的糖尿病术语使用模式。设计/方法/方法-问答分析方法允许对医疗保健消费者的大量数据进行第一手调查。使用多维缩放(MDS)可视化方法进行了跨类别的可视术语聚类分析,该方法提供了一种直观的交互方式来探索和发现可视环境中的术语关联模式。确定了密切相关的类别,并在术语级别分析了类别(“体征与症状”与“器官与身体部位”;“诊断与测试”以及“诊断与用药”)之间的相应视觉术语聚类分析。调查结果-调查结果表明,两个相关类别中的术语之间存在密切关系。相关术语进行了分组并揭示了模式。 MDS分析的所有应力值均低于0.10,每种组合类别的RSQ均超过0.90,这表明所研究的术语在视觉分析中得到了很好的聚类。原创性/价值-该研究为类似的消费者健康研究提供了独特的研究方法。这项研究的结果提供了对消费者健康术语使用行为的洞察力,并丰富了现有叙词表和主题词表,增强了与糖尿病相关的网站或门户,并提高了内部搜索引擎的效率。

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