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This study created a dataset to identify optimal data sources for clinical trial eligibility criteria. Machine learning models accurately predict whether criteria need structured data or manual review of clinical notes.

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Area of Science:

  • Clinical informatics
  • Biomedical data science
  • Medical artificial intelligence

Background:

  • Clinical trial screening relies on both structured data and manual review of unstructured clinical notes for subject eligibility.
  • Automating trial screening requires identifying the most appropriate data source for each eligibility criterion.

Purpose of the Study:

  • To develop an annotated dataset for clinical trial eligibility criteria, specifying preferred data sources (structured vs. unstructured).
  • To create and evaluate machine learning models for predicting the optimal data source for resolving eligibility criteria.

Main Methods:

  • A dataset was created with 50 heart failure trials (766 criteria) and 50 chronic lymphocytic leukemia (CLL) trials (677 criteria).
  • Medical annotators designated the preferred data source for each criterion.
  • Machine learning models, including kernel methods, were developed using lexical, syntactic, semantic, and surface features to predict the data source.

Main Results:

  • Kernel methods demonstrated superior performance compared to simpler models in predicting the preferred data source.
  • Model performance was consistent across heart failure and CLL trial data, indicating generalizability.
  • The developed models show promise for automating the identification of data sources for eligibility criteria.

Conclusions:

  • The created dataset and machine learning models represent a significant advancement in automating clinical trial screening.
  • The findings support the potential for generalizable AI-driven solutions in clinical trial data management.
  • Accurate data source identification is crucial for efficient and effective clinical trial subject selection.