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Baseline Regularization for Computational Drug Repositioning with Longitudinal Observational Data.

Zhaobin Kuang1, James Thomson2, Michael Caldwell3

  • 1University of Wisconsin-Madison.

IJCAI : Proceedings of the Conference
|April 11, 2017
PubMed
Summary
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Computational drug repositioning (CDR) uses electronic health records (EHRs) to find new uses for existing drugs. New methods effectively identified drugs that lower blood glucose levels.

Area of Science:

  • Pharmacology
  • Biomedical Informatics
  • Data Science

Background:

  • Computational Drug Repositioning (CDR) identifies new therapeutic indications for existing medications.
  • Electronic Health Records (EHRs) offer valuable longitudinal data for CDR.
  • Analyzing EHRs presents challenges due to high dimensionality and heterogeneity.

Purpose of the Study:

  • To develop novel methods for Computational Drug Repositioning using EHR data.
  • To address the complexities of longitudinal, high-dimensional EHR data in CDR.
  • To identify existing drugs that can effectively lower Fasting Blood Glucose (FBG) levels.

Main Methods:

  • Proposed Baseline Regularization (BR) and a variant method.
  • Extended the one-way fixed effect model for EHR analysis.

Related Experiment Videos

  • Applied methods to Marshfield Clinic EHR data to find FBG-lowering drugs.

    • Main Results:

    • Successfully rediscovered known FBG-lowering drugs.
    • Identified potential novel blood sugar-lowering agents from literature.
    • Demonstrated the efficacy of BR methods in EHR-based CDR.

    Conclusions:

    • The proposed BR methods are effective for CDR using EHR data.
    • This approach can accelerate the discovery of new drug indications.
    • EHR data holds significant potential for advancing drug repositioning strategies.