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Recent advances in biomedical literature mining.

Sendong Zhao1, Chang Su2, Zhiyong Lu3

  • 1Department of Healthcare Policy and Research, Weill Medical College of Cornell University, New York, NY 10065, USA.

Briefings in Bioinformatics
|May 19, 2020
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Summary

Biomedical literature mining (BLM) extracts knowledge from rapidly growing electronic health articles. This review examines advances from biomedical informatics and computer science to guide future research.

Keywords:
Biomedical Literature MiningDeep LearningNatural Language Processing

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Area of Science:

  • Biomedical Informatics
  • Computer Science
  • Text Mining

Background:

  • The exponential growth of biomedical literature necessitates advanced methods for knowledge extraction.
  • Electronic accessibility of research papers fuels the demand for effective biomedical literature mining (BLM) techniques.
  • Domain knowledge within biomedical texts is crucial for research and practical applications.

Purpose of the Study:

  • To review recent advancements in biomedical literature mining (BLM).
  • To analyze contributions from both biomedical informatics (BMI) and computer science (CS) communities.
  • To identify and inspire future research directions in BLM.

Main Methods:

  • Literature review of recent publications in biomedical informatics and computer science.
  • Comparative analysis of methodologies favored by BMI (interpretability) and CS (performance).
  • Synthesis of current trends and identification of research gaps.

Main Results:

  • Diverse approaches exist, with BMI focusing on application-specific, interpretable methods.
  • CS community develops sophisticated models prioritizing performance and generalization.
  • A gap exists between interpretable, application-focused BMI methods and high-performance, generalizable CS models.

Conclusions:

  • Bridging the gap between BMI and CS approaches can lead to more robust and applicable BLM tools.
  • Future research should focus on integrating interpretability with advanced performance.
  • Cross-community collaboration is essential for advancing the field of biomedical literature mining.