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Predicting the sample size of randomized controlled trials using natural language processing.

Paul Windisch1, Fabio Dennstädt2, Carole Koechli1

  • 1Department of Radiation Oncology, Cantonal Hospital Winterthur, 8400 Winterthur, Switzerland.

JAMIA Open
|October 28, 2024
PubMed
Summary
This summary is machine-generated.

Developing a named entity recognition (NER) model to extract sample sizes from randomized controlled trials (RCTs) abstracts is feasible. This NER model can improve systematic reviews and search functionalities.

Keywords:
GPT-4evidence-based medicinemachine learningnatural language processingrandomized controlled trialtext miningtransformer

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Area of Science:

  • Medical Informatics
  • Clinical Trial Analysis
  • Natural Language Processing

Background:

  • Extracting sample size from randomized controlled trials (RCTs) abstracts is crucial for systematic reviews and search functionalities.
  • Current methods often depend on explicit mentions of sample size, limiting their effectiveness.
  • Developing automated approaches is essential for efficient data extraction.

Purpose of the Study:

  • To develop and validate novel approaches for extracting sample size information from RCT abstracts.
  • To assess the performance of a trained Named Entity Recognition (NER) model in predicting trial participant numbers.
  • To evaluate the efficacy of GPT-4o in extracting sample size data from RCT abstracts.

Main Methods:

  • 847 RCTs from high-impact journals were analyzed, with 6 entities indicative of sample size tagged.
  • A Named Entity Recognition (NER) model was trained to extract these entities.
  • The NER model and GPT-4o were evaluated on a test set of 150 RCTs for prediction accuracy.

Main Results:

  • The most accurate NER model predicted sample sizes for 64.7% of trials, with 93.8% accuracy against ground truth.
  • GPT-4o achieved a prediction rate of 94.7% with 90.8% accuracy compared to ground truth.
  • Combinations of extracted entities improved predictive model performance.

Conclusions:

  • Training an NER model to predict RCT sample sizes from abstracts is a viable method.
  • NER models can be customized by combining entities for varied characteristics.
  • Large language models like GPT-4o offer comparable performance but may incur higher costs.