ArticleAMIA ... Annual Symposium proceedings. AMIA Symposium2014
Mining consumer health vocabulary from community-generated text.
Article in AMIA ... Annual Symposium proceedings. AMIA Symposium, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
14 citing papers in PubMed.
- Traditional, Complementary, and Integrative Medicine on X (Formerly Twitter) From 2015 to 2024 Across English, Spanish, and French: Retrospective Observational Study.Journal of medical Internet research · 2026Observational
- What do cancer patients discuss online regarding CINV management? A social media-based topic modeling study.Frontiers in oncology · 2026Article
- Evaluating Expert-Layperson Agreement in Identifying Jargon Terms in Electronic Health Record Notes: Observational Study.Journal of medical Internet research · 2024Observational
- Clinician Perspectives and Design Implications in Using Patient-Generated Health Data to Improve Mental Health Practices: Mixed Methods Study.JMIR formative research · 2020Article
- Situating Wikipedia as a health information resource in various contexts: A scoping review.PloS one · 2020Article
- Hybrid bag of approaches to characterize selection criteria for cohort identification.Journal of the American Medical Informatics Association : JAMIA · 2019Article
- Development of a Consumer Health Vocabulary by Mining Health Forum Texts Based on Word Embedding: Semiautomatic Approach.JMIR medical informatics · 2019Article
- Mining and standardizing chinese consumer health terms.BMC medical informatics and decision making · 2018Article
- The User Knows What to Call It: Incorporating Patient Voice Through User-Contributed Tags on a Participatory Platform About Health Management.Journal of medical Internet research · 2017Article
- Enriching consumer health vocabulary through mining a social Q&A site: A similarity-based approach.Journal of biomedical informatics · 2017Article
- When synonyms are not enough: Optimal parenthetical insertion for text simplification.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2017Article
- Consumers' Use of UMLS Concepts on Social Media: Diabetes-Related Textual Data Analysis in Blog and Social Q&A Sites.JMIR medical informatics · 2016Article
- Aspiring to Unintended Consequences of Natural Language Processing: A Review of Recent Developments in Clinical and Consumer-Generated Text Processing.Yearbook of medical informatics · 2016Review
- A Scalable Framework to Detect Personal Health Mentions on Twitter.Journal of medical Internet research · 2015Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
Community-generated text corpora can be a valuable resource to extract consumer health vocabulary (CHV) and link them to professional terminologies and alternative variants. In this research, we propose a pattern-based text-mining approach to identify pairs of CHV and professional terms from Wikipedia, a large text corpus created and maintained by the community. A novel measure, leveraging the ratio of frequency of occurrence, was used to differentiate consumer terms from professional terms. We empirically evaluated the applicability of this approach using a large data sample consisting of MedLine abstracts and all posts from an online health forum, MedHelp. The results show that the proposed approach is able to identify synonymous pairs and label the terms as either consumer or professional term with high accuracy. We conclude that the proposed approach provides great potential to produce a high quality CHV to improve the performance of computational applications in processing consumer-generated health text.
Indexed as
Identifiers
25954426PMC4419967What OpenQuestion holds
Registered trials
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.