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Integrating domain knowledge for biomedical text analysis into deep learning: A survey.

Linkun Cai1, Jia Li2, Han Lv2

  • 1School of Biological Science and Medical Engineering, Beihang University, 100191 Beijing, China.

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Summary

This review explores integrating domain knowledge with deep learning for biomedical text analysis. Combining knowledge graphs enhances deep learning models, overcoming data limitations for better insights.

Keywords:
Biomedical text analysisDeep learningDomain knowledgeNatural language processing

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Area of Science:

  • Biomedical Natural Language Processing
  • Artificial Intelligence in Medicine
  • Knowledge Representation and Reasoning

Background:

  • The biomedical field has seen a surge in textual data, crucial for healthcare and research.
  • Deep learning shows promise in biomedical text analysis but faces challenges with data annotation and interpretability.
  • Integrating domain knowledge, like biomedical knowledge graphs, offers a solution to enhance deep learning models.

Purpose of the Study:

  • To comprehensively review recent literature on incorporating domain knowledge into deep learning for biomedical text analysis.
  • To identify common biomedical text analysis tasks benefiting from this integration.
  • To discuss current challenges and future research directions in this interdisciplinary field.

Main Methods:

  • Systematic literature search focusing on studies published within the last decade.
  • Analysis of over 150 research papers.
  • Categorization of studies based on the biomedical text analysis tasks addressed (e.g., information extraction, text classification, text generation).

Main Results:

  • The integration of domain knowledge, particularly biomedical knowledge graphs, is a growing trend in deep learning for biomedical NLP.
  • This approach effectively addresses limitations of data scarcity and improves model interpretability.
  • Key applications include enhanced information extraction, more accurate text classification, and improved text generation in the biomedical domain.

Conclusions:

  • Incorporating domain knowledge into deep learning models is a powerful strategy for advancing biomedical text analysis.
  • Future research should focus on developing more sophisticated methods for knowledge integration and addressing remaining challenges in interpretability and scalability.
  • This interdisciplinary approach holds significant potential for accelerating biomedical discovery and improving healthcare outcomes.