ArticleJournal of medical Internet research2011
Computer-assisted update of a consumer health vocabulary through mining of social network data.
Article in Journal of medical Internet research, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers, 2 of them syntheses that pooled it.
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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.
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Who cites it
31 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- A systematic review of natural language processing and text mining of symptoms from electronic patient-authored text data.International journal of medical informatics · 2019Pooled it
- A new dimension of health care: systematic review of the uses, benefits, and limitations of social media for health communication.Journal of medical Internet research · 2013Pooled it
- Evaluating Expert-Layperson Agreement in Identifying Jargon Terms in Electronic Health Record Notes: Observational Study.Journal of medical Internet research · 2024Observational
- A survey of automated methods for biomedical text simplification.Journal of the American Medical Informatics Association : JAMIA · 2022Article
- Search Term Identification Methods for Computational Health Communication: Word Embedding and Network Approach for Health Content on YouTube.JMIR medical informatics · 2022Article
- Share to Seek: The Effects of Disease Complexity on Health Information-Seeking Behavior.Journal of medical Internet research · 2021Article
- An automated method to enrich consumer health vocabularies using GloVe word embeddings and an auxiliary lexical resource.PeerJ. Computer science · 2021Article
- COVID-19 Surveillance in a Primary Care Sentinel Network: In-Pandemic Development of an Application Ontology.JMIR public health and surveillance · 2020Article
- Clinician Perspectives and Design Implications in Using Patient-Generated Health Data to Improve Mental Health Practices: Mixed Methods Study.JMIR formative research · 2020Article
- Development of a Consumer Health Vocabulary by Mining Health Forum Texts Based on Word Embedding: Semiautomatic Approach.JMIR medical informatics · 2019Article
- Ranking Medical Terms to Support Expansion of Lay Language Resources for Patient Comprehension of Electronic Health Record Notes: Adapted Distant Supervision Approach.JMIR medical informatics · 2017Article
- 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
- Understanding Health Care Social Media Use From Different Stakeholder Perspectives: A Content Analysis of an Online Health Community.Journal of medical Internet research · 2017Article
- Evaluation of Facebook and Twitter Monitoring to Detect Safety Signals for Medical Products: An Analysis of Recent FDA Safety Alerts.Drug safety · 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
- Leveraging cues from person-generated health data for peer matching in online communities.Journal of the American Medical Informatics Association : JAMIA · 2016Article
- Automated concept and relationship extraction for the semi-automated ontology management (SEAM) system.Journal of biomedical semantics · 2015Article
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
backgroundConsumer health vocabularies (CHVs) have been developed to aid consumer health informatics applications. This purpose is best served if the vocabulary evolves with consumers' language.
objectiveOur objective was to create a computer assisted update (CAU) system that works with live corpora to identify new candidate terms for inclusion in the open access and collaborative (OAC) CHV.
methodsThe CAU system consisted of three main parts: a Web crawler and an HTML parser, a candidate term filter that utilizes natural language processing tools including term recognition methods, and a human review interface. In evaluation, the CAU system was applied to the health-related social network website PatientsLikeMe.com. The system's utility was assessed by comparing the candidate term list it generated to a list of valid terms hand extracted from the text of the crawled webpages.
resultsThe CAU system identified 88,994 unique terms 1- to 7-grams ("n-grams" are n consecutive words within a sentence) in 300 crawled PatientsLikeMe.com webpages. The manual review of the crawled webpages identified 651 valid terms not yet included in the OAC CHV or the Unified Medical Language System (UMLS) Metathesaurus, a collection of vocabularies amalgamated to form an ontology of medical terms, (ie, 1 valid term per 136.7 candidate n-grams). The term filter selected 774 candidate terms, of which 237 were valid terms, that is, 1 valid term among every 3 or 4 candidates reviewed.
conclusionThe CAU system is effective for generating a list of candidate terms for human review during CHV development.
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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.