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Extracting the Population, Intervention, Comparison and Sentiment from Randomized Controlled Trials.

Markus Zlabinger1, Linda Andersson1, Jon Brassey2

  • 1Institute of Software Technology and Interactive Systems, TU Wien, Vienna.

Studies in Health Technology and Informatics
|April 22, 2018
PubMed
Summary

This study introduces a word-level approach for identifying Population, Intervention, and Comparison in Randomized Controlled Trials (RCTs). It also classifies trial sentiment to determine intervention effectiveness, achieving high scores in experiments.

Keywords:
Information extractionmachine learningnatural language processingsentiment analysis

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Area of Science:

  • Natural Language Processing
  • Biomedical Informatics
  • Clinical Trial Analysis

Background:

  • Randomized Controlled Trials (RCTs) are crucial for evidence-based medicine.
  • Extracting key information like Population, Intervention, and Comparison (PIC) from RCTs is essential for systematic reviews and meta-analyses.
  • Previous methods often relied on sentence-level analysis, which can be less precise.

Purpose of the Study:

  • To propose a novel word-level approach for identifying Population, Intervention, and Comparison (PIC) elements within Randomized Controlled Trials (RCTs).
  • To develop a sentiment classification method for RCTs to ascertain the comparative effectiveness of interventions.
  • To evaluate the proposed methods using newly created corpora.

Main Methods:

  • Developed a word-level identification model for PIC elements in RCTs.
  • Implemented a sentiment classification algorithm to assess intervention efficacy relative to comparisons.
  • Created and utilized two new corpora for rigorous evaluation of the proposed approaches.

Main Results:

  • Achieved an average F1 score of 0.85 for the Population, Intervention, and Comparison (PIC) identification task.
  • Obtained an average F1 score of 0.72 for the sentiment classification of RCTs.
  • Demonstrated the effectiveness of word-level analysis over sentence-level approaches.

Conclusions:

  • The proposed word-level identification approach significantly improves the extraction of PIC elements from RCTs.
  • Sentiment classification of RCTs provides valuable insights into intervention effectiveness.
  • The developed methods and corpora contribute to more accurate and efficient analysis of clinical trial data.