Machine Learning Approaches for Extracting Stage from Pathology Reports in Prostate Cancer.

Journal: Studies in health technology and informatics
Published Date: Aug 21, 2019

Abstract

Clinical and pathological stage are defining parameters in oncology, which direct a patient's treatment options and prognosis. Pathology reports contain a wealth of staging information that is not stored in structured form in most electronic health records (EHRs). Therefore, we evaluated three supervised machine learning methods (Support Vector Machine, Decision Trees, Gradient Boosting) to classify free-text pathology reports for prostate cancer into T, N and M stage groups.

Authors

  • Raphael Lenain
    Department of Medicine, Biomedical Informatics, Stanford University, Stanford, CA, USA.
  • Martin G Seneviratne
    Department of Biomedical Informatics, Stanford School of Medicine, CA.
  • Selen Bozkurt
    Department of Biostatistics and Medical Informatics, Akdeniz University Faculty of Medinice, 48000 Antalya, Turkey.
  • Douglas W Blayney
    Stanford University, School of Medicine, Stanford, CA.
  • James D Brooks
    Department of Urology, Stanford School of Medicine, CA.
  • Tina Hernandez-Boussard
    Stanford Center for Biomedical Informatics Research, Stanford, California 94305, USA.

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