ArticleJMIR cancer2018
Assessing Unmet Information Needs of Breast Cancer Survivors: Exploratory Study of Online Health Forums Using Text Classification and Retrieval.
Article in JMIR cancer, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis that pooled it.
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Who cites it
14 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Pooled it
- Connected and supported: a scoping review of how online communities provide social support for breast cancer survivors.Journal of cancer survivorship : research and practice · 2026Article
- Enhancing Detection of Message Intents in a Mobile Health Smoking-Cessation Intervention Using Large Language Model Fine-Tuning, Data Downsampling, and Error Correction: Algorithm Development and Validation.Journal of medical Internet research · 2026Article
- Supervised machine learning applied in nursing notes for identifying the need of childhood cancer patients for psychosocial support.Frontiers in digital health · 2025Article
- Information needs during cancer care: Qualitative research with locally advanced cervical cancer patients in Brazil, China, Germany, & the US.Gynecologic oncology reports · 2024Article
- Extracting the latent needs of dementia patients and caregivers from transcribed interviews in japanese: an initial assessment of the availability of morpheme selection as input data with Z-scores in machine learning.BMC medical informatics and decision making · 2023Article
- Multilevel Classification of Users' Needs in Chinese Online Medical and Health Communities: Model Development and Evaluation Based on Graph Convolutional Network.JMIR formative research · 2023Article
- Predicting Patient Satisfaction With Medications for Treating Opioid Use Disorder: Case Study Applying Natural Language Processing to Reviews of Methadone and Buprenorphine/Naloxone on Health-Related Social Media.JMIR infodemiology · 2023Article
- A Digital Cancer Ecosystem to Deliver Health and Psychosocial Education as Preventive Intervention.Cancers · 2022Article
- Determination of Patient Sentiment and Emotion in Ophthalmology: Infoveillance Tutorial on Web-Based Health Forum Discussions.Journal of medical Internet research · 2021Article
- Assessing how health information needs of individuals with colorectal cancer are met across the care continuum: an international cross-sectional survey.BMC cancer · 2020Article
- Information Needs of Breast Cancer Patients: Theory-Generating Meta-Synthesis.Journal of medical Internet research · 2020Review
- Effects of a WhatsApp-Delivered Education Intervention to Enhance Breast Cancer Knowledge in Women: Mixed-Methods Study.JMIR mHealth and uHealth · 2020Article
- Teleconsultations between Patients and Healthcare Professionals in Primary Care in Catalonia: The Evaluation of Text Classification Algorithms Using Supervised Machine Learning.International journal of environmental research and public health · 2020Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
backgroundPatient education materials given to breast cancer survivors may not be a good fit for their information needs. Needs may change over time, be forgotten, or be misreported, for a variety of reasons. An automated content analysis of survivors' postings to online health forums can identify expressed information needs over a span of time and be repeated regularly at low cost. Identifying these unmet needs can guide improvements to existing education materials and the creation of new resources.
objectiveThe primary goals of this project are to assess the unmet information needs of breast cancer survivors from their own perspectives and to identify gaps between information needs and current education materials.
methodsThis approach employs computational methods for content modeling and supervised text classification to data from online health forums to identify explicit and implicit requests for health-related information. Potential gaps between needs and education materials are identified using techniques from information retrieval.
resultsWe provide a new taxonomy for the classification of sentences in online health forum data. 260 postings from two online health forums were selected, yielding 4179 sentences for coding. After annotation of data and training alternative one-versus-others classifiers, a random forest-based approach achieved F1 scores from 66% (Other, dataset2) to 90% (Medical, dataset1) on the primary information types. 136 expressions of need were used to generate queries to indexed education materials. Upon examination of the best two pages retrieved for each query, 12% (17/136) of queries were found to have relevant content by all coders, and 33% (45/136) were judged to have relevant content by at least one.
conclusionsText from online health forums can be analyzed effectively using automated methods. Our analysis confirms that breast cancer survivors have many information needs that are not covered by the written documents they typically receive, as our results suggest that at most a third of breast cancer survivors' questions would be addressed by the materials currently provided to them.
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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.