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Performing Data Mining And Integrative Analysis Of Biomarker in Breast Cancer Using Multiple Publicly Accessible Databases
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Automatic data extraction to support meta-analysis statistical analysis: a case study on breast cancer.

Faith Wavinya Mutinda1, Kongmeng Liew1, Shuntaro Yada1

  • 1Graduate School of Science and Technology, Nara Institute of Science and Technology, Nara, Japan.

BMC Medical Informatics and Decision Making
|June 18, 2022
PubMed
Summary

This study introduces an automated system to extract clinical trial data from abstracts, speeding up meta-analyses. While data extraction is accurate, statistical analysis needs improvement due to incomplete abstract information.

Keywords:
Automatic data extractionAutomatic meta-analysisEvidence-based medicineNamed entity recognition (NER)Natural language processing (NLP)

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Area of Science:

  • Biomedical Informatics
  • Clinical Research
  • Data Science

Background:

  • Meta-analyses synthesize clinical study results but are time-consuming and labor-intensive.
  • The rapid increase in research articles leads to outdated meta-analyses.
  • Automated data extraction can expedite meta-analyses and enable automatic updates.

Purpose of the Study:

  • To develop a system for automatic data extraction from research abstracts.
  • To perform statistical analysis on extracted data for meta-analysis.
  • To address the challenges of time and labor in meta-analysis.

Main Methods:

  • Utilized a corpus of 1011 PubMed abstracts of breast cancer randomized controlled trials.
  • Developed a BERT-based named entity recognition (NER) model to identify Participants, Intervention, Control, and Outcomes (PICO).
  • Parsed numeric outcomes for statistical analysis after PICO extraction.

Main Results:

  • The NER model achieved F1-scores greater than 0.80 for most PICO entities.
  • The data extraction step demonstrated high accuracy.
  • The statistical analysis step showed low performance due to missing information in abstracts.

Conclusions:

  • A system for automatic data extraction from research abstracts and statistical analysis was proposed.
  • System performance was evaluated by reproducing an existing meta-analysis.
  • The system showed relatively good performance, but requires further substantiation.