Prediction of progression from pre-diabetes to diabetes: Development and validation of a machine learning model.

Journal: Diabetes/metabolism research and reviews
Published Date: Feb 1, 2020

Abstract

AIMS: Identification, a priori, of those at high risk of progression from pre-diabetes to diabetes may enable targeted delivery of interventional programmes while avoiding the burden of prevention and treatment in those at low risk. We studied whether the use of a machine-learning model can improve the prediction of incident diabetes utilizing patient data from electronic medical records.

Authors

  • Avivit Cahn
    Diabetes Unit, Dept. of Endocrinology and Metabolism, Hadassah University Hospital, Hebrew University of Jerusalem, The Faculty of Medicine, Jerusalem, Israel.
  • Avi Shoshan
    Medial EarlySign, Hod Hasharon, Israel.
  • Tal Sagiv
    Medial EarlySign, Hod Hasharon, Israel.
  • Rachel Yesharim
    Medial EarlySign, Hod Hasharon, Israel.
  • Ran Goshen
    Medial EarlySign, Kfar Malal, Israel.
  • Varda Shalev
    Institute of Health Research and Innovation, Maccabi Healthcare Services, Tel-Aviv, Israel.
  • Itamar Raz
    Diabetes Unit, Dept. of Endocrinology and Metabolism, Hadassah University Hospital, Hebrew University of Jerusalem, The Faculty of Medicine, Jerusalem, Israel.

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