ArticleJMIR cancer2021
Medical Needs Extraction for Breast Cancer Patients from Question and Answer Services: Natural Language Processing-Based Approach.
Article in JMIR cancer, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 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
4 citing papers in PubMed, 8 citations in OpenAlex.
- Article
- Article
- Search Term Identification Methods for Computational Health Communication: Word Embedding and Network Approach for Health Content on YouTube.JMIR medical informatics · 2022Article
- A sentiment analysis and topic modeling of weibo discourse on public perceptions of breast cancer.Digital healthArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundA large number of patient narratives are available on various web services. As for web question and answer services, patient questions often relate to medical needs, and we expect these questions to provide clues for a better understanding of patients' medical needs.
objectiveThis study aimed to extract patients' needs and classify them into thematic categories. Clarifying patient needs is the first step in solving social issues that patients with cancer encounter.
methodsFor this study, we used patient question texts containing the key phrase "breast cancer," available at the Yahoo! Japan question and answer service, Yahoo! Chiebukuro, which contains over 60,000 questions on cancer. First, we converted the question text into a vector representation. Next, the relevance between patient needs and existing cancer needs categories was calculated based on cosine similarity.
resultsThe proportion of correct classifications in our proposed method was approximately 70%. Considering the results of classifying questions, we found the variation and the number of needs.
conclusionsWe created 3 corpora to classify the problems of patients with cancer. The proposed method was able to classify the problems considering the question text. Moreover, as an application example, the question text that included the side effect signaling of drugs and the unmet needs of cancer patients could be extracted. Revealing these needs is important to fulfill the medical needs of patients with cancer.
Indexed as
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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.