Toward rapid learning in cancer treatment selection: An analytical engine for practice-based clinical data.

Journal: Journal of biomedical informatics
Published Date: Feb 2, 2016

Abstract

OBJECTIVE: Wide-scale adoption of electronic medical records (EMRs) has created an unprecedented opportunity for the implementation of Rapid Learning Systems (RLSs) that leverage primary clinical data for real-time decision support. In cancer, where large variations among patient features leave gaps in traditional forms of medical evidence, the potential impact of a RLS is particularly promising. We developed the Melanoma Rapid Learning Utility (MRLU), a component of the RLS, providing an analytical engine and user interface that enables physicians to gain clinical insights by rapidly identifying and analyzing cohorts of patients similar to their own.

Authors

  • Samuel G Finlayson
    Harvard Medical School, Boston, MA, United States.
  • Mia Levy
    Vanderbilt University School of Medicine, Nashville, TN, United States.
  • Sunil Reddy
    Stanford University School of Medicine, Stanford, CA, United States.
  • Daniel L Rubin
    Department of Biomedical Data Science, Stanford University School of Medicine Medical School Office Building, Stanford CA 94305-5479.

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