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Integrating domain knowledge for biomedical text analysis into deep learning: A survey.
Cai, Linkun; Li, Jia; Lv, Han; Liu, Wenjuan; Niu, Haijun; Wang, Zhenchang.
Afiliação
  • Cai L; School of Biological Science and Medical Engineering, Beihang University, 100191 Beijing, China.
  • Li J; Department of Radiology, Beijing Friendship Hospital, Capital Medical University, 100050 Beijing, China.
  • Lv H; Department of Radiology, Beijing Friendship Hospital, Capital Medical University, 100050 Beijing, China.
  • Liu W; Aerospace Center Hospital, 100049 Beijing, China.
  • Niu H; School of Biological Science and Medical Engineering, Beihang University, 100191 Beijing, China.
  • Wang Z; School of Biological Science and Medical Engineering, Beihang University, 100191 Beijing, China; Department of Radiology, Beijing Friendship Hospital, Capital Medical University, 100050 Beijing, China. Electronic address: cjr.wzhch@vip.163.com.
J Biomed Inform ; 143: 104418, 2023 07.
Article em En | MEDLINE | ID: mdl-37290540
The past decade has witnessed an explosion of textual information in the biomedical field. Biomedical texts provide a basis for healthcare delivery, knowledge discovery, and decision-making. Over the same period, deep learning has achieved remarkable performance in biomedical natural language processing, however, its development has been limited by well-annotated datasets and interpretability. To solve this, researchers have considered combining domain knowledge (such as biomedical knowledge graph) with biomedical data, which has become a promising means of introducing more information into biomedical datasets and following evidence-based medicine. This paper comprehensively reviews more than 150 recent literature studies on incorporating domain knowledge into deep learning models to facilitate typical biomedical text analysis tasks, including information extraction, text classification, and text generation. We eventually discuss various challenges and future directions.
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Texto completo: 1 Bases de dados: MEDLINE Assunto principal: Pesquisa Biomédica / Aprendizado Profundo Tipo de estudo: Prognostic_studies Idioma: En Revista: J Biomed Inform Assunto da revista: INFORMATICA MEDICA Ano de publicação: 2023 Tipo de documento: Article País de afiliação: China

Texto completo: 1 Bases de dados: MEDLINE Assunto principal: Pesquisa Biomédica / Aprendizado Profundo Tipo de estudo: Prognostic_studies Idioma: En Revista: J Biomed Inform Assunto da revista: INFORMATICA MEDICA Ano de publicação: 2023 Tipo de documento: Article País de afiliação: China