Reverse Engineering and Evaluation of Prediction Models for Progression to Type 2 Diabetes: An Application of Machine Learning Using Electronic Health Records.

Journal: Journal of diabetes science and technology
Published Date: Dec 20, 2015

Abstract

BACKGROUND: Application of novel machine learning approaches to electronic health record (EHR) data could provide valuable insights into disease processes. We utilized this approach to build predictive models for progression to prediabetes and type 2 diabetes (T2D).

Authors

  • Jeffrey P Anderson
    GNS Healthcare, Cambridge, MA, USA janderson@gnshealthcare.com jpa696@mail.harvard.edu.
  • Jignesh R Parikh
    GNS Healthcare, Cambridge, MA, USA.
  • Daniel K Shenfeld
    GNS Healthcare, Cambridge, MA, USA.
  • Vladimir Ivanov
    GNS Healthcare, Cambridge, MA, USA.
  • Casey Marks
    GNS Healthcare, Cambridge, MA, USA.
  • Bruce W Church
    GNS Healthcare, Cambridge, MA, USA.
  • Jason M Laramie
    GNS Healthcare, Cambridge, MA, USA.
  • Jack Mardekian
    Pfizer Inc, New York, NY, USA.
  • Beth Anne Piper
    Pfizer Inc, New York, NY, USA.
  • Richard J Willke
    Pfizer Inc, New York, NY, USA.
  • Dale A Rublee
    Pfizer Inc, New York, NY, USA.

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