Development of a machine learning algorithm for early detection of opioid use disorder.

Journal: Pharmacology research & perspectives
Published Date: Dec 1, 2020

Abstract

BACKGROUND: Opioid use disorder (OUD) affects an estimated 16 million people worldwide. The diagnosis of OUD is commonly delayed or missed altogether. We aimed to test the utility of machine learning in creating a prediction model and algorithm for early diagnosis of OUD.

Authors

  • Zvi Segal
    Diagnostic Robotics Inc., Ariel, Israel.
  • Kira Radinsky
    Department of Computer Science , Technion - Israel Institute of Technology , Haifa 3200003 , Israel.
  • Guy Elad
    Diagnostic Robotics Inc., Ariel, Israel.
  • Gal Marom
    Diagnostic Robotics Inc., Ariel University, Aviv, Israel.
  • Moran Beladev
    Diagnostic Robotics Inc., Ariel University, Aviv, Israel.
  • Maor Lewis
    Diagnostic Robotics Inc., Ariel, Israel.
  • Bar Ehrenberg
    Diagnostic Robotics Inc., Ariel, Israel.
  • Plia Gillis
    Diagnostic Robotics Inc., Ariel University, Aviv, Israel.
  • Liat Korn
    Ariel University, Ariel, Israel.
  • Gideon Koren
    From the Department of Healthcare Informatics, IBM Research, IBM R&D Labs, University of Haifa Campus, Mount Carmel, Haifa 3498825, Israel (A.A.B., M.C., Y.S., A.S., A.H., R.M., E.B., S.N., E.K., Y.G., M.R.Z.); MaccabiTech, MKM, Maccabi Healthcare Services, Tel Aviv, Israel (E.H., G.K., V.S.); and Department of Imaging, Assuta Medical Centers, Tel Aviv, Israel (M.G.).

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