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A Multi-Label Text Classifier at Publication Level Based on "PubMedBERT + TextRNN" for Cancer Literature.
Ying, Zhang; Guanghui, Xia; Xiaoying, Li; Shishi, Tang.
Afiliación
  • Ying Z; Institute of Medical Information, Chinese Academy of Medical Sciences, China.
  • Guanghui X; Institute of Medical Information, Chinese Academy of Medical Sciences, China.
  • Xiaoying L; Institute of Medical Information, Chinese Academy of Medical Sciences, China.
  • Shishi T; Institute of Medical Information, Chinese Academy of Medical Sciences, China.
Stud Health Technol Inform ; 316: 374-375, 2024 Aug 22.
Article en En | MEDLINE | ID: mdl-39176755
ABSTRACT
There is a rapid growth in the volume of data in the cancer field and fine-grained classification is in high demand especially for interdisciplinary and collaborative research. There is thus a need to establish a multi-label classifier with higher resolution to reduce the burden of screening articles for clinical relevance. This research trains a multi-label classifier with scalability for classifying literature on cancer research directly at the publication level. Firstly, a corpus was divided into a training set and a testing set at a ratio of 73. Secondly, we compared the performance of classifiers developed by "PubMedBERT + TextRNN" and "BioBERT + TextRNN" with ICRP CT. Finally, the classifier was obtained based on the optimal combination "PubMedBERT + TextRNN", with P= 0.952014, R=0.936696, F1=0.931664. The quantitative comparisons demonstrate that the resulting classifier is fit for high-resolution classification of cancer literature at the publication level to support accurate retrieving and academic statistics.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Neoplasias Límite: Humans Idioma: En Revista: Stud Health Technol Inform Asunto de la revista: INFORMATICA MEDICA / PESQUISA EM SERVICOS DE SAUDE Año: 2024 Tipo del documento: Article País de afiliación: China Pais de publicación: Países Bajos
Texto completo
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Neoplasias Límite: Humans Idioma: En Revista: Stud Health Technol Inform Asunto de la revista: INFORMATICA MEDICA / PESQUISA EM SERVICOS DE SAUDE Año: 2024 Tipo del documento: Article País de afiliación: China Pais de publicación: Países Bajos