Automated data extraction and ensemble methods for predictive modeling of breast cancer outcomes after radiation therapy.

Journal: Medical physics
Published Date: Dec 28, 2018

Abstract

PURPOSE: The purpose of this study was to compare the effectiveness of ensemble methods (e.g., random forests) and single-model methods (e.g., logistic regression and decision trees) in predictive modeling of post-RT treatment failure and adverse events (AEs) for breast cancer patients using automatically extracted EMR data.

Authors

  • William D Lindsay
    Oncora Medical, Philadelphia, Pennsylvania.
  • Christopher A Ahern
    Oncora Medical, Philadelphia, PA, 19103, USA.
  • Jacob S Tobias
    Oncora Medical, Philadelphia, PA, 19103, USA.
  • Christopher G Berlind
    Oncora Medical, Philadelphia, PA, 19103, USA.
  • Chidambaram Chinniah
    Department of Radiation Oncology, Hospital of the University of Pennsylvania, Philadelphia, PA, 19104, USA.
  • Peter E Gabriel
    Department of Radiation Oncology, Hospital of the University of Pennsylvania, Philadelphia, PA, 19104, USA.
  • James C Gee
    Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104, USA.
  • Charles B Simone
    Department of Radiation Oncology, University of Maryland Medical Center.

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