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Biomedical evidence engineering for data-driven discovery.

Sendong Zhao1, Aobo Wang2, Bing Qin1

  • 1Department of Population Health Sciences, College of Computer Science and Technology, Harbin Institute of Technology, Harbin 10065, China.

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Summary
This summary is machine-generated.

Automating biomedical evidence discovery from health data is crucial. This study presents a framework using literature retrieval and BERT-based extraction to efficiently verify insights, supported by a large annotated dataset.

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Area of Science:

  • Biomedical Informatics
  • Computational Biology
  • Precision Medicine

Background:

  • Precision medicine generates vast health data, necessitating efficient methods for insight verification.
  • Manual verification of data-driven insights against biomedical literature is time-consuming and not scalable.
  • Intelligent techniques are required to automate the process of evidence discovery.

Purpose of the Study:

  • To introduce a framework for biomedical evidence engineering to automate insight verification.
  • To develop efficient modules for retrieving and extracting evidence from biomedical literature.
  • To address the limitations of manual verification in the era of big health data.

Main Methods:

  • A framework combining biomedical literature retrieval and evidence extraction modules.
  • Ensemble methods for state-of-the-art biomedical literature retrieval.
  • A BERT-based model for extracting evidence in response to specific queries.
  • Creation of a large-scale dataset with 1 million biomedical evidence examples, including 10,000 manually annotated instances.

Main Results:

  • The retrieval module achieved state-of-the-art performance.
  • A BERT-based model was successfully developed for evidence extraction.
  • A comprehensive dataset for biomedical evidence engineering was created and made available.
  • The proposed framework offers an intelligent solution for evidence discovery.

Conclusions:

  • The developed framework significantly enhances the efficiency of verifying data-driven insights from biomedical literature.
  • Automated evidence engineering is essential for advancing precision medicine and data-driven discovery.
  • The publicly available dataset will facilitate further research in biomedical evidence extraction and validation.