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Updated: May 22, 2026

A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts
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A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts

Published on: September 20, 2018

A methodological framework for conducting diagnostic meta-analyses using large literature corpora.

Jennyfer Portilla-Yela1, José-Rafael Tovar-Cuevas2

  • 1School of Industrial Engineering, University of Valle, Cali, Valle del Cauca, Colombia. jennyfer.portilla@correounivalle.edu.co.

BMC Medical Research Methodology
|May 21, 2026
PubMed
Summary

This study introduces an automated framework for diagnostic meta-analysis, significantly improving efficiency and accuracy in synthesizing evidence from large biomedical literature sets. The approach enhances the reliability of diagnostic performance metrics.

Keywords:
Dengue diagnosisDiagnostic accuracyHierarchical meta-analysisLDAMachine learning in healthcareSystematic review automationTopic modeling

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A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts
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Comparing Bibliometric Analysis Using PubMed, Scopus, and Web of Science Databases
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Published on: October 24, 2019

Area of Science:

  • Biomedical informatics
  • Medical statistics
  • Evidence synthesis

Background:

  • Exponential growth in biomedical literature complicates manual meta-analysis.
  • Challenges include feasibility, reproducibility, and bias control in diagnostic meta-analyses.
  • Manual screening is a bottleneck for synthesizing diagnostic evidence.

Purpose of the Study:

  • To develop a scalable framework for diagnostic meta-analysis using automated methods.
  • To integrate topic modeling and hierarchical meta-analysis for efficient evidence synthesis.
  • To improve the synthesis of diagnostic accuracy data (sensitivity and specificity).

Main Methods:

  • Proposed a framework combining Latent Dirichlet Allocation (LDA) topic modeling for pre-screening with hierarchical multivariate meta-analysis.
  • Utilized linguistic normalization, lemmatization, and probabilistic topic modeling on abstracts from eight databases.
  • Employed bivariate hierarchical random-effects models on the logit scale to synthesize sensitivity and specificity, incorporating moderators.

Main Results:

  • The framework processed 5,766 records, identifying 10 studies with 94 dengue diagnostic algorithms.
  • Machine-learning models demonstrated significantly higher joint diagnostic performance compared to traditional models.
  • External validation showed a significant performance decrease; sensitivity ranged from 37.97% to 86.66% and specificity from 63.26% to 94.81%.

Conclusions:

  • The proposed workflow provides a rigorous and scalable method for diagnostic evidence synthesis.
  • This approach is adaptable to clinical fields with extensive literature and varied evidence.
  • Automated methods enhance the synthesis of diagnostic performance data, addressing literature growth challenges.