Background

Healthcare consumers are increasingly turning to the online health Q&A communities to seek answers for their questions because current general search engines are unable to digest complex health-related questions. Q&A communities are platforms where users ask unstructured questions from different healthcare topics.

Objectives

This study aimed to provide a concept-based approach to automatically assign health questions to the appropriate domain experts.

Methods

We developed three processes for (1) expert profiling, (2) question analysis and (3) similarity calculation and assignment. Semantic weight of concepts combined with TF-IDF weighting comprised vectors of concepts as expert profiles. Subsequently, the similarity between submitted questions and expert profiles was calculated to find a relevant expert.

Results

We randomly selected 345 questions posted by consumers for 38 experts in 13 health topics from NetWellness as input data. Our results showed the precision and recall of our proposed method for the studied topics were between 63%-92% and 61%-100%, respectively. The calculated F-measure in selected topics was between 62% (Addiction and Substance Abuse) and 94% (Eye and Vision Care) with a combined F-measure of 80%.

Conclusions

Concept-based methods using unified medical language system and natural language processing techniques could automatically assign actual health questions in different topics to the relevant domain experts with good performance metrics.

中文翻译：

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基于概念的医疗保健问题自动分配给在线问答社区的领域专家。

背景

医疗保健消费者越来越多地向在线医疗保健问答社区寻求问题的答案，因为当前的通用搜索引擎无法消化与健康有关的复杂问题。问答社区是用户从不同的医疗保健主题中提出非结构化问题的平台。

目标

这项研究旨在提供一种基于概念的方法，将健康问题自动分配给适当的领域专家。

方法

我们针对（1）专家分析，（2）问题分析和（3）相似度计算和分配开发了三个过程。概念的语义权重与TF-IDF加权相结合，将概念的向量作为专家配置文件。随后，计算提交的问题和专家简介之间的相似度以找到相关专家。

结果

我们从NetWellness的13个健康主题中随机选择了345个消费者提出的345个问题作为输入数据。我们的结果表明，针对所研究主题的方法的准确性和召回率分别在63％-92％和61％-100％之间。在选定主题中，计算出的F值介于62％（成瘾和药物滥用）到94％（眼与视力保健）之间，而F值的总和为80％。

结论

使用统一医学语言系统和自然语言处理技术的基于概念的方法可以自动将不同主题中的实际健康问题分配给具有良好性能指标的相关领域专家。