ArticleJMIR medical informatics2016
Consumers' Use of UMLS Concepts on Social Media: Diabetes-Related Textual Data Analysis in Blog and Social Q&A Sites.
Article in JMIR medical informatics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed.
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- Identifying Medical Concepts and Semantic Types in Lay Vocabularies of Health Consumers Who are Concerned with Diabetes on Social Media Using the UMLS and NLP.Proceedings : Annual International Computer Software and Applications Conference. COMPSAC · 2024Article
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- Search Term Identification Methods for Computational Health Communication: Word Embedding and Network Approach for Health Content on YouTube.JMIR medical informatics · 2022Article
- Using logical constraints to validate statistical information about disease outbreaks in collaborative knowledge graphs: the case of COVID-19 epidemiology in Wikidata.PeerJ. Computer science · 2022Article
- Medical Needs Extraction for Breast Cancer Patients from Question and Answer Services: Natural Language Processing-Based Approach.JMIR cancer · 2021Article
- Understanding patient needs and gaps in radiology reports through online discussion forum analysis.Insights into imaging · 2021Article
- Share to Seek: The Effects of Disease Complexity on Health Information-Seeking Behavior.Journal of medical Internet research · 2021Article
- Patient Challenges and Needs in Comprehending Laboratory Test Results: Mixed Methods Study.Journal of medical Internet research · 2020Article
- Topic Modeling for Analyzing Patients' Perceptions and Concerns of Hearing Loss on Social Q&A Sites: Incorporating Patients' Perspective.International journal of environmental research and public health · 2020Article
- Retrieving Lab Test Related Questions from Social Q&A Sites by Combining Shallow Features and Deep Representations.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2020Article
- Analyzing Social Media Data to Understand Consumer Information Needs on Dietary Supplements.Studies in health technology and informatics · 2019Article
- Understanding Patient Information Needs About Their Clinical Laboratory Results: A Study of Social Q&A Site.Studies in health technology and informatics · 2019Article
- Social media for health promotion in diabetes: study protocol for a participatory public health intervention design.BMC health services research · 2018Article
- Plain-language medical vocabulary for precision diagnosis.Nature genetics · 2018Article
- Assessing the practice of biomedical ontology evaluation: Gaps and opportunities.Journal of biomedical informatics · 2018Review
- Validating UMLS Semantic Type Assignments Using SNOMED CT Semantic Tags.Methods of information in medicine · 2018Article
- Enriching consumer health vocabulary through mining a social Q&A site: A similarity-based approach.Journal of biomedical informatics · 2017Article
- Analysis of Temporal Constraints in Qualitative Eligibility Criteria of Cancer Clinical Studies.Proceedings. IEEE International Conference on Bioinformatics and Biomedicine · 2016Article
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5 authors.
Funding
Abstract
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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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.