Using supervised machine learning classifiers to estimate likelihood of participating in clinical trials of a de-identified version of ResearchMatch.

Journal: Journal of clinical and translational science
Published Date: Sep 4, 2020

Abstract

INTRODUCTION: Lack of participation in clinical trials (CTs) is a major barrier for the evaluation of new pharmaceuticals and devices. Here we report the results of the analysis of a dataset from ResearchMatch, an online clinical registry, using supervised machine learning approaches and a deep learning approach to discover characteristics of individuals more likely to show an interest in participating in CTs.

Authors

  • Janette Vazquez
    Department of Biomedical Informatics, University of Utah, Salt Lake City, UT, USA.
  • Samir Abdelrahman
    Department of Biomedical Informatics, University of Utah, Salt Lake City, UT, USA.
  • Loretta M Byrne
    Vanderbilt University, Nashville, TN, USA.
  • Michael Russell
    Vanderbilt University, Nashville, TN, USA.
  • Paul Harris
    Vanderbilt University, Nashville, TN, USA.
  • Julio C Facelli
    Department of Biomedical Informatics, University of Utah, Salt Lake City, UT, USA.

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