ArticleAMIA ... Annual Symposium proceedings. AMIA Symposium2014
Characterizing the sublanguage of online breast cancer forums for medications, symptoms, and emotions.
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 15 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.
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Who cites it
15 citing papers in PubMed.
- Cancers missed, women dismissed yet persist: natural language processing of online forums.Breast cancer research : BCR · 2025Article
- Voice Analysis of Cancer Experiences Among Patients With Breast Cancer: VOICE-BC.Journal of patient experience · 2021Article
- Development of a Consumer Health Vocabulary by Mining Health Forum Texts Based on Word Embedding: Semiautomatic Approach.JMIR medical informatics · 2019Article
- Utility of social media and crowd-intelligence data for pharmacovigilance: a scoping review.BMC medical informatics and decision making · 2018Article
- Longitudinal analysis of discussion topics in an online breast cancer community using convolutional neural networks.Journal of biomedical informatics · 2017Article
- "We make choices we think are going to save us": Debate and stance identification for online breast cancer CAM discussions.Proceedings of the ... International World-Wide Web Conference. International WWW Conference · 2017Article
- Machine Learning for Identifying Emotional Expression in Text: Improving the Accuracy of Established Methods.Journal of technology in behavioral science · 2017Article
- Online cancer communities as informatics intervention for social support: conceptualization, characterization, and impact.Journal of the American Medical Informatics Association : JAMIA · 2017Article
- 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
- Speculation detection for Chinese clinical notes: Impacts of word segmentation and embedding models.Journal of biomedical informatics · 2016Article
- Content-specific network analysis of peer-to-peer communication in an online community for smoking cessation.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2016Article
- Characterization of Temporal Semantic Shifts of Peer-to-Peer Communication in a Health-Related Online Community: Implications for Data-driven Health Promotion.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2016Article
- Automatically Detecting Failures in Natural Language Processing Tools for Online Community Text.Journal of medical Internet research · 2015Article
- Does sustained participation in an online health community affect sentiment?AMIA ... Annual Symposium proceedings. AMIA Symposium · 2014Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Online health communities play an increasingly prevalent role for patients and are the source of a growing body of research. A lexicon that represents the sublanguage of an online community is an important resource to enable analysis and tool development over this data source. This paper investigates a method to generate a lexicon representative of the language of members in a given community with respect to specific semantic types. We experiment with a breast cancer community and detect terms that belong to three semantic types: medications, symptoms and side effects, and emotions. We assess the ability of our automatically generated lexicons to detect new terms, and show that a data-driven approach captures the sublanguage of members in these communities, all the while increasing coverage of general-purpose terminologies. The code and the generated lexicons are made available to the research community.
Indexed as
Identifiers
25954356PMC4419934What 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.