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

HIGHLIGHTS

• who: Unknown from the Division of Drug Informatics, Keio University Faculty of Pharmacy, Japan have published the paper: 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, in the Journal: (JOURNAL)
• what: This study showed that the BERT model can extract multiple worries from text generated from patients with breast This is the first application of a multilabel classifier using the BERT model to extract multiple worries from patient-generated text. Consent to use the data from Life . . .
  

   

  

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