ArticleAMIA ... Annual Symposium proceedings. AMIA Symposium2017
Talking About My Care: Detecting Mentions of Hormonal Therapy Adherence Behavior in an Online Breast Cancer Community.
Article in AMIA ... Annual Symposium proceedings. AMIA Symposium, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.
What it found
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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
8 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A systematic literature review of machine learning in online personal health data.Journal of the American Medical Informatics Association : JAMIA · 2019Pooled it
- Perceptions and Utilization of Online Peer Support Among Informal Dementia Caregivers: Survey Study.JMIR aging · 2024Article
- Extending Adjuvant Endocrine Therapy for 10 Years: A Mixed-Methods Analysis of Women's Decision Making in an Online Breast Cancer Forum.Healthcare (Basel, Switzerland) · 2021Article
- A focused netnographic study exploring experiences associated with counterfeit and contaminated anabolic-androgenic steroids.Harm reduction journal · 2020Article
- A frame semantic overview of NLP-based information extraction for cancer-related EHR notes.Journal of biomedical informatics · 2019Review
- Patient Messaging Content Associated with Initiating Hormonal Therapy after a Breast Cancer Diagnosis.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2019Article
- The therapy is making me sick: how online portal communications between breast cancer patients and physicians indicate medication discontinuation.Journal of the American Medical Informatics Association : JAMIA · 2018Article
- Learning When Communications Between Healthcare Providers Indicate Hormonal Therapy Medication Discontinuation.AMIA ... Annual Symposium proceedings. AMIA Symposium · 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
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Hormonal therapy adherence is challenging for many patients with hormone-receptor-positive breast cancer. Gaining intuition into their adherence behavior would assist in improving outcomes by pinpointing, and eventually addressing, why patients fail to adhere. While traditional adherence studies rely on survey-based methods or electronic medical records, online health communities provide a supplemental data source to learn about such behavior and often on a much larger scale. In this paper, we focus on an online breast cancer discussion forum and propose a framework to automatically extract hormonal therapy adherence behavior (HTAB) mentions. The framework compares medical term usage when describing when a patient is taking hormonal therapy medication and interrupting their treatment (e.g., stop/pause taking medication). We show that by using shallow neural networks, in the form of wordlvec, the learned features can be applied to build efficient HTAB mention classifiers. Through medical term comparison, we find that patients who exhibit an interruption behavior are more likely to mention depression and their care providers, while patients with continuation behavior are more likely to mention common side effects (e.g., hot flashes, nausea and osteoporosis), vitamins and exercise.
Indexed as
Identifiers
29854258PMC5977653What 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.