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Automated mapping of electronic data capture fields to SDTM.

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  • 1Medidata Solutions, New York, New York, United States of America.

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Summary

Siamese networks automate clinical trial data mapping, significantly improving accuracy and reducing manual work. This machine learning approach enhances data capture to submission efficiency.

Area of Science:

  • Machine Learning
  • Bioinformatics
  • Clinical Data Management

Background:

  • Manual data mapping in clinical trials is labor-intensive.
  • Traditional methods lack efficiency for large datasets and numerous classes.

Purpose of the Study:

  • To reduce manual effort in clinical trial data capture and regulatory submission.
  • To demonstrate the efficacy of Siamese networks for data field classification.

Main Methods:

  • Utilized Siamese networks to generate embeddings from electronic data capture form metadata.
  • Employed traditional machine learning classifiers on generated embeddings.
  • Focused on learning data similarity for improved classification.

Main Results:

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  • Achieved classification accuracies greater than 90%, a significant improvement over traditional methods.
  • Demonstrated a 15% increase in accuracy in many cases.
  • Identified lack of training data as a cause for some inaccuracies.

Conclusions:

  • Siamese networks efficiently generate low-dimensional data field embeddings.
  • Automated data schema mapping is achievable with high accuracy.
  • This represents a key step towards automating clinical trial data management tasks.