Development of a "meta-model" to address missing data, predict patient-specific cancer survival and provide a foundation for clinical decision support.

Journal: Journal of the American Medical Informatics Association : JAMIA
Published Date:

Abstract

OBJECTIVE: Like most real-world data, electronic health record (EHR)-derived data from oncology patients typically exhibits wide interpatient variability in terms of available data elements. This interpatient variability leads to missing data and can present critical challenges in developing and implementing predictive models to underlie clinical decision support for patient-specific oncology care. Here, we sought to develop a novel ensemble approach to addressing missing data that we term the "meta-model" and apply the meta-model to patient-specific cancer prognosis.

Authors

  • Jason M Baron
    Department of Pathology, Massachusetts General Hospital, Boston Harvard Medical School, Boston, MA. jmbaron@partners.org.
  • Ketan Paranjape
  • Tara Love
    Roche Diagnostics Corporation, Santa Clara, California, USA.
  • Vishakha Sharma
    Roche Diagnostics Corporation, Santa Clara, California, USA.
  • Denise Heaney
    Roche Diagnostics Corporation, North America, Indianapolis, Indiana, USA.
  • Matthew Prime
    Roche Diagnostics Corporation, Riehen, Basel Stadt, Switzerland.