Computer-Assisted Update of a Consumer Health Vocabulary Through Mining of Social Network Data

Doing-Harris, Kristina; Zeng‐Treitler, Qing

doi:10.2196/jmir.1636

Cited by 74 publications

(59 citation statements)

References 18 publications

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“…After we obtained all the true positives and true negatives in top 100, we were able to calculate the precision, recall and F-1 measure in top k (k = 5, 10,15,20). TABLE III illustrates the evaluation results.…”

Section: Discussionmentioning

confidence: 99%

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Using Co-occurrence Analysis to Expand Consumer Health Vocabularies from Social Media Data

Jiang

Yang

2013

2013 IEEE International Conference on Healthcare Informatics

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As health consumerism in the United States has remarkably risen over the past decade, more and more health consumers are actively seeking health related information on their own. However, health consumers' efforts in seeking healthcare information could be challenging due to the lack of professional knowledge in medicine, and the gap between health professional vocabulary and health consumer vocabulary is one of the biggest issues. It has long been recognized that consumers and health professionals often express health concepts in different ways and consumers usually find it difficult to understand some medical terminologies or express themselves using the professional language for information seeking. To address this problem, many researchers have been working on developing Consumer Health Vocabulary (CHV) to bridge the language gap. One crucial step in developing CHV is the identification of consumer health expressions. Most of the methods used by existing studies in identifying consumer health expressions involve human efforts, which is very time-consuming. In this study, we propose an automatic method to identify consumer health expressions from consumer-contributed content, which represent consumers' language the best. Co-occurrence analysis was used to identify terms that co-occur frequently with a set of seed terms. A corpus containing 120,393 discussion messages was used as a dataset and co-occurrence analysis was used to extract the most related consumer expressions. The experiment results demonstrated that the proposed method achieved a good performance in identifying consumer health expressions.

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Section: Discussionmentioning

confidence: 99%

“…They first crawled corpora from PatientLikeMe, and then identified valid candidate terms from the data through automatic term recognition (ATR) using C-value and termhood score. Last, they identified consumer health expressions by expert review [15].…”

Section: A Consumer Health Vocabularymentioning

confidence: 99%

Using Co-occurrence Analysis to Expand Consumer Health Vocabularies from Social Media Data

Jiang

Yang

2013

2013 IEEE International Conference on Healthcare Informatics

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“…To keep up with continuous evolution of medical knowledge, CHV needs to be updated and maintained by incorporating new, consumer-provided terms and expressions [17,22-24]. Existing studies have shown promising results in discovering consumer terms for CHV from social media, in particular.…”

Section: Introductionmentioning

confidence: 99%

“…Hicks et al [25] analyzed consumer messages exchanged in Twitter in order to evaluate terms related to gender identification on intake forms. Doing-Harris and Zeng-Treitler [24] developed a computer assisted CHV update system, which can automatically identify prospective terms from social media. Identifying terms used by consumers in consumer-generated text in aggregate fashion can account for the variability of lay health language.…”

Section: Introductionmentioning

confidence: 99%

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Consumers’ Use of UMLS Concepts on Social Media: Diabetes-Related Textual Data Analysis in Blog and Social Q&A Sites

Park¹,

He²,

Chen³

et al. 2016

JMIR Med Inform

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BackgroundThe widely known terminology gap between health professionals and health consumers hinders effective information seeking for consumers.ObjectiveThe aim of this study was to better understand consumers’ usage of medical concepts by evaluating the coverage of concepts and semantic types of the Unified Medical Language System (UMLS) on diabetes-related postings in 2 types of social media: blogs and social question and answer (Q&A).MethodsWe collected 2 types of social media data: (1) a total of 3711 blogs tagged with “diabetes” on Tumblr posted between February and October 2015; and (2) a total of 58,422 questions and associated answers posted between 2009 and 2014 in the diabetes category of Yahoo! Answers. We analyzed the datasets using a widely adopted biomedical text processing framework Apache cTAKES and its extension YTEX. First, we applied the named entity recognition (NER) method implemented in YTEX to identify UMLS concepts in the datasets. We then analyzed the coverage and the popularity of concepts in the UMLS source vocabularies across the 2 datasets (ie, blogs and social Q&A). Further, we conducted a concept-level comparative coverage analysis between SNOMED Clinical Terms (SNOMED CT) and Open-Access Collaborative Consumer Health Vocabulary (OAC CHV)—the top 2 UMLS source vocabularies that have the most coverage on our datasets. We also analyzed the UMLS semantic types that were frequently observed in our datasets.ResultsWe identified 2415 UMLS concepts from blog postings, 6452 UMLS concepts from social Q&A questions, and 10,378 UMLS concepts from the answers. The medical concepts identified in the blogs can be covered by 56 source vocabularies in the UMLS, while those in questions and answers can be covered by 58 source vocabularies. SNOMED CT was the dominant vocabulary in terms of coverage across all the datasets, ranging from 84.9% to 95.9%. It was followed by OAC CHV (between 73.5% and 80.0%) and Metathesaurus Names (MTH) (between 55.7% and 73.5%). All of the social media datasets shared frequent semantic types such as “Amino Acid, Peptide, or Protein,” “Body Part, Organ, or Organ Component,” and “Disease or Syndrome.”ConclusionsAlthough the 3 social media datasets vary greatly in size, they exhibited similar conceptual coverage among UMLS source vocabularies and the identified concepts showed similar semantic type distributions. As such, concepts that are both frequently used by consumers and also found in professional vocabularies such as SNOMED CT can be suggested to OAC CHV to improve its coverage.

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Exploring collective tagging as a mechanism to elicit language about health management

Chen

Carriere

Kaplan

et al. 2016

Proc. Assoc. Info. Sci. Tech.

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This paper describes an innovative effort to invite the public to participate in the creation of a public resource -to leverage collective wisdom in the health domain. This project involved building a website where people could contribute their experiences of body listening and how they learned/were learning to do it. Within the context of this study, body listening was described as the act of paying attention to the body's signals and cues, which can improve individuals' long-term health management. Over the course of the study, participants and moderators together authored 431 posts and contributed 818 tags. This paper presents an initial analysis of these tags.This analysis makes several intellectual contributions. First, we present a preliminary classification scheme for concepts associated with body listening and selfmanagement that might be used in future efforts to organize knowledge in this domain. Second, we evaluate interannotator agreement using this classification scheme, to characterize its potential for use in future work.

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Computer-Assisted Update of a Consumer Health Vocabulary Through Mining of Social Network Data

Cited by 74 publications

References 18 publications

Using Co-occurrence Analysis to Expand Consumer Health Vocabularies from Social Media Data

Using Co-occurrence Analysis to Expand Consumer Health Vocabularies from Social Media Data

Consumers’ Use of UMLS Concepts on Social Media: Diabetes-Related Textual Data Analysis in Blog and Social Q&A Sites

Exploring collective tagging as a mechanism to elicit language about health management

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