ArticleJournal of biomedical informatics2017
Enriching consumer health vocabulary through mining a social Q&A site: A similarity-based approach.
Article in Journal of biomedical informatics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
18 citing papers in PubMed.
- ShenNongDiet: a medicine diet recommendation system based on graph-semantic hybrid retrieval and evidence constraint generation.Chinese medicine · 2026Article
- README: Bridging Medical Jargon and Lay Understanding for Patient Education through Data-Centric NLP.Findings of ACL. EMNLP. Conference on Empirical Methods in Natural Language Processing · 2024Article
- Evaluating Expert-Layperson Agreement in Identifying Jargon Terms in Electronic Health Record Notes: Observational Study.Journal of medical Internet research · 2024Observational
- Improving the Quality and Utility of Electronic Health Record Data through Ontologies.Standards (Basel, Switzerland) · 2023Article
- Users' Willingness to Share Health Information in a Social Question-and-Answer Community: Cross-sectional Survey in China.JMIR medical informatics · 2021Article
- Ambiguity in medical concept normalization: An analysis of types and coverage in electronic health record datasets.Journal of the American Medical Informatics Association : JAMIA · 2021Article
- Utilizing a multi-class classification approach to detect therapeutic and recreational misuse of opioids on Twitter.Computers in biology and medicine · 2021Article
- An automated method to enrich consumer health vocabularies using GloVe word embeddings and an auxiliary lexical resource.PeerJ. Computer science · 2021Article
- Clinician Perspectives and Design Implications in Using Patient-Generated Health Data to Improve Mental Health Practices: Mixed Methods Study.JMIR formative research · 2020Article
- An Informatics Framework to Assess Consumer Health Language Complexity Differences: Proof-of-Concept Study.Journal of medical Internet research · 2020Article
- Mining social media for prescription medication abuse monitoring: a review and proposal for a data-centric framework.Journal of the American Medical Informatics Association : JAMIA · 2020Review
- Understanding Patient Information Needs About Their Clinical Laboratory Results: A Study of Social Q&A Site.Studies in health technology and informatics · 2019Article
- Analyzing Social Media Data to Understand Consumer Information Needs on Dietary Supplements.Studies in health technology and informatics · 2019Article
- Artificial Intelligence for Participatory Health: Applications, Impact, and Future Implications.Yearbook of medical informatics · 2019Review
- Development of a Consumer Health Vocabulary by Mining Health Forum Texts Based on Word Embedding: Semiautomatic Approach.JMIR medical informatics · 2019Article
- Extended Analysis of Topological-Pattern-Based Ontology Enrichment.Proceedings. IEEE International Conference on Bioinformatics and Biomedicine · 2018Article
- Evaluating semantic relations in neural word embeddings with biomedical and general domain knowledge bases.BMC medical informatics and decision making · 2018Article
- Validating UMLS Semantic Type Assignments Using SNOMED CT Semantic Tags.Methods of information in medicine · 2018Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
The widely known vocabulary gap between health consumers and healthcare professionals hinders information seeking and health dialogue of consumers on end-user health applications. The Open Access and Collaborative Consumer Health Vocabulary (OAC CHV), which contains health-related terms used by lay consumers, has been created to bridge such a gap. Specifically, the OAC CHV facilitates consumers' health information retrieval by enabling consumer-facing health applications to translate between professional language and consumer friendly language. To keep up with the constantly evolving medical knowledge and language use, new terms need to be identified and added to the OAC CHV. User-generated content on social media, including social question and answer (social Q&A) sites, afford us an enormous opportunity in mining consumer health terms. Existing methods of identifying new consumer terms from text typically use ad-hoc lexical syntactic patterns and human review. Our study extends an existing method by extracting n-grams from a social Q&A textual corpus and representing them with a rich set of contextual and syntactic features. Using K-means clustering, our method, simiTerm, was able to identify terms that are both contextually and syntactically similar to the existing OAC CHV terms. We tested our method on social Q&A corpora on two disease domains: diabetes and cancer. Our method outperformed three baseline ranking methods. A post-hoc qualitative evaluation by human experts further validated that our method can effectively identify meaningful new consumer terms on social Q&A.
Indexed as
Identifiers
What 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.