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How to (Semi)-Automatically Spot Prescreening Oriented Eligibility Criteria.

Morgan Vaterkowski1,2, Nadir Ammour2, Christel Daniel1,3

  • 1Sorbonne Université, INSERM, Université Sorbonne Paris-Nord, Laboratoire d'informatique médicale et d'ingénierie des connaissances en e-santé, LIMICS, 75006 Paris, France.

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Summary
This summary is machine-generated.

This study introduces a method to semi-automatically identify Prescreening-Oriented Eligibility Criteria (POEC) from clinical trial documents. A POEC library is created to improve the development and evaluation of patient recruitment support systems using electronic health records.

Keywords:
ClinicalClinical trials as topicDecision Support SystemsElectronic Health RecordPatient SelectionProgram Evaluation

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Area of Science:

  • Biomedical Informatics
  • Clinical Trial Operations
  • Health Data Science

Background:

  • Clinical Trial Recruitment Support Systems (CTRSS) increasingly use Electronic Health Records (EHR) for patient-trial matching.
  • Manual processing of free-text clinical trial eligibility criteria (EC) for EHR querying is time-consuming and inefficient.
  • Automating patient-trial matching requires structured eligibility criteria suitable for EHR querying.

Purpose of the Study:

  • To develop a methodological approach for semi-automatically detecting Prescreening-Oriented Eligibility Criteria (POEC).
  • To build a reusable library of POEC for the development and evaluation of EHR-based CTRSS.
  • To facilitate the use of EHR data for improved clinical trial participation.

Main Methods:

  • Decomposition of free-text EC into standardized elements.
  • Development of a rule-based algorithm for semi-automatic POEC detection.
  • Annotation of 381 free-text EC from 20 cancer clinical trials using a framework of 96 elementary EC patterns across 17 domains.
  • Creation of a publicly available POEC library (PENELOPE POEC library) fed by the PENELOPE-C2Q pipeline.

Main Results:

  • A methodological approach for semi-automatically identifying POEC was established.
  • A rule-based algorithm for POEC detection was developed and trained on annotated EC data.
  • A POEC library was created, suitable for evaluating EHR-based CTRSS.
  • The PENELOPE-C2Q pipeline and PENELOPE POEC library were introduced to support EHR data reuse.

Conclusions:

  • The proposed methodology enables semi-automatic detection and standardization of POEC.
  • The POEC library provides a valuable resource for developing and evaluating CTRSS.
  • This approach can enhance the efficiency of patient-trial matching and facilitate EHR data reuse in clinical research.