Jiang Keyuan, Chen Tingyu, Calix Ricardo A, Bernard Gordon R
Computer Information Technology & Graphics, Purdue University Northwest, U.S.A.
Department of Medicine, Vanderbilt University, U.S.A.
Stud Health Technol Inform. 2018;251:273-276.
Prevalence of social media has driven a growing number of health related applications with the information shared by online users. It is well known that a gap exists between healthcare professionals and laypeople in expressing the same health concepts. Filling this gap is particularly important for health related applications using social media data. A data-driven, attributional similarity-based method was developed to identify Twitter terms related to side effect concepts. For the 10 most common side effect (symptom) concepts, our method was able to identify a total of 333 Twitter terms, among which only 90 are mapped to those in the consumer health vocabulary (CHV). The identified Twitter terms are specific to Twitter data, indicating a need to expand the existing CHV, and many of them seem to have less ambiguity in word senses than those in CHV.
社交媒体的普及推动了越来越多与健康相关的应用程序，这些应用程序使用在线用户共享的信息。众所周知，医疗保健专业人员和外行人在表达相同的健康概念时存在差距。对于使用社交媒体数据的健康相关应用程序而言，填补这一差距尤为重要。我们开发了一种基于数据驱动、归因相似性的方法来识别与副作用概念相关的推特术语。对于10个最常见的副作用（症状）概念，我们的方法总共能够识别出333个推特术语，其中只有90个与消费者健康词汇表（CHV）中的术语相匹配。所识别出的推特术语特定于推特数据，这表明需要扩展现有的CHV，而且其中许多术语在词义上似乎比CHV中的术语歧义更少。
