This paper is in the following e-collection/theme issue:

Breast Cancer (247) Medicine 2.0: Social Media, Open, Participatory, Collaborative Medicine (1793) Natural Language Processing (804) Infodemiology and Infoveillance (1460) Infoveillance, Infodemiology, Digital Disease Surveillance, Infodemic Management (1088)

Published on in Vol 8, No 2 (2022): Apr-Jun

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/37840, first published .
Extracting Multiple Worries From Breast Cancer Patient Blogs Using Multilabel Classification With the Natural Language Processing Model Bidirectional Encoder Representations From Transformers: Infodemiology Study of Blogs

Extracting Multiple Worries From Breast Cancer Patient Blogs Using Multilabel Classification With the Natural Language Processing Model Bidirectional Encoder Representations From Transformers: Infodemiology Study of Blogs

Extracting Multiple Worries From Breast Cancer Patient Blogs Using Multilabel Classification With the Natural Language Processing Model Bidirectional Encoder Representations From Transformers: Infodemiology Study of Blogs

Authors of this article:

Tomomi Watanabe1 Author Orcid Image ;   Shuntaro Yada2 Author Orcid Image ;   Eiji Aramaki2 Author Orcid Image ;   Hiroshi Yajima3 Author Orcid Image ;   Hayato Kizaki1 Author Orcid Image ;   Satoko Hori1 Author Orcid Image
Article Authors Cited by (9) Tweetations (8) Metrics

Journals

  1. Li C, Weng Y, Zhang Y, Wang B. A Systematic Review of Application Progress on Machine Learning-Based Natural Language Processing in Breast Cancer over the Past 5 Years. Diagnostics 2023;13(3):537 View
  2. Hamamoto R, Koyama T, Kouno N, Yasuda T, Yui S, Sudo K, Hirata M, Sunami K, Kubo T, Takasawa K, Takahashi S, Machino H, Kobayashi K, Asada K, Komatsu M, Kaneko S, Yatabe Y, Yamamoto N. Introducing AI to the molecular tumor board: one direction toward the establishment of precision medicine using large-scale cancer clinical and biological information. Experimental Hematology & Oncology 2022;11(1) View
  3. Nishioka S, Asano M, Yada S, Aramaki E, Yajima H, Yanagisawa Y, Sayama K, Kizaki H, Hori S. Adverse event signal extraction from cancer patients’ narratives focusing on impact on their daily-life activities. Scientific Reports 2023;13(1) View
  4. Zhang Z, Liew K, Kuijer R, She W, Yada S, Wakamiya S, Aramaki E. Differing Content and Language Based on Poster-Patient Relationships on the Chinese Social Media Platform Weibo: Text Classification, Sentiment Analysis, and Topic Modeling of Posts on Breast Cancer. JMIR Cancer 2024;10:e51332 View
  5. Nishioka S, Watabe S, Yanagisawa Y, Sayama K, Kizaki H, Imai S, Someya M, Taniguchi R, Yada S, Aramaki E, Hori S. Adverse Event Signal Detection Using Patients’ Concerns in Pharmaceutical Care Records: Evaluation of Deep Learning Models. Journal of Medical Internet Research 2024;26:e55794 View
  6. Zeinali N, Albashayreh A, Fan W, White S. Symptom-BERT: Enhancing Cancer Symptom Detection in EHR Clinical Notes. Journal of Pain and Symptom Management 2024;68(2):190 View
  7. Marchena Sekli G. The research landscape on generative artificial intelligence: a bibliometric analysis of transformer-based models. Kybernetes 2024 View
  8. Watabe S, Watanabe T, Yada S, Aramaki E, Yajima H, Kizaki H, Hori S, Ekbal A. Exploring a method for extracting concerns of multiple breast cancer patients in the domain of patient narratives using BERT and its optimization by domain adaptation using masked language modeling. PLOS ONE 2024;19(9):e0305496 View
  9. Zitu M, Le T, Duong T, Haddadan S, Garcia M, Amorrortu R, Zhao Y, Rollison D, Thieu T. Large language models in cancer: potentials, risks, and safeguards. BJR|Artificial Intelligence 2025;2(1) View