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Machine learning-based donor permission extraction from informed consent documents.

Meng Zhang1, Madhuri Sankaranarayanapillai1, Jingcheng Du1

  • 1McWilliam School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, 77030, USA.

BMC Bioinformatics
|December 15, 2023
PubMed
Summary
This summary is machine-generated.

This study developed a natural language processing (NLP) tool to automatically identify key permission sentences in informed consent forms. The machine learning model achieved high accuracy, aiding biorepository data regulation and sharing.

Keywords:
Informed consentMachine learningNatural language processingText classification

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Clinical Trial Management

Background:

  • Clinical trials increasingly involve bio-specimen collection for biobanking.
  • Informed consent forms for these trials are becoming more complex.
  • Standardizing consent for data and bio-specimen sharing is crucial for research.

Purpose of the Study:

  • To develop an automated natural language processing (NLP) tool for annotating informed consent documents.
  • To enhance biorepository data regulation, sharing, and decision support.
  • To identify sentences related to permissions for bio-specimen and data sharing, and future research.

Main Methods:

  • Collected informed consent documents from public sources.
  • Manually annotated documents for sentences concerning permissions.
  • Evaluated machine learning algorithms, including Random Forest (RF) and Support Vector Machine (SVM).

Main Results:

  • Annotated 120 informed consent documents (29,204 sentences).
  • Identified 1250 sentences (4.28%) related to permission questions.
  • Achieved a F1-score of 0.95 using an SVM model on a prefiltered corpus.

Conclusions:

  • Demonstrated the feasibility of using machine learning for classifying permission-related sentences.
  • The NLP tool can support regulatory compliance and data sharing in biobanking.
  • Automated annotation improves efficiency and accuracy in consent form analysis.