A Machine-Learning-Based Drug Repurposing Approach Using Baseline Regularization.

Journal: Methods in molecular biology (Clifton, N.J.)
Published Date: Jan 1, 2019

Abstract

We present the baseline regularization model for computational drug repurposing using electronic health records (EHRs). In EHRs, drug prescriptions of various drugs are recorded throughout time for various patients. In the same time, numeric physical measurements (e.g., fasting blood glucose level) are also recorded. Baseline regularization uses statistical relationships between the occurrences of prescriptions of some particular drugs and the increase or the decrease in the values of some particular numeric physical measurements to identify potential repurposing opportunities.

Authors

  • Zhaobin Kuang
    The University of Wisconsin, Madison, WI, USA. zkuang@wisc.edu.
  • Yujia Bao
    The Massachusetts Institute of Technology, Cambridge, MA, USA.
  • James Thomson
    Morgridge Institute for Research, Regenerative Biology, Madison, WI, USA.
  • Michael Caldwell
    The Marshfield Clinic, Marshfield, WI, USA.
  • Peggy Peissig
    Biomedical Informatics Research Center, Marshfield Clinic Research Foundation, Marshfield, WI, 54449, USA.
  • Ron Stewart
    Morgridge Institute for Research, Regenerative Biology, Madison, WI, USA.
  • Rebecca Willett
    Department of Statistics, University of Chicago, Chicago, Illinois.
  • David Page
    Duke University, Department of Biostatistics and Bioinformatics, Durham, NC, USA.

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