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

Zhaobin Kuang; Yujia Bao; James Thomson; Michael Caldwell; Peggy Peissig; Ron Stewart; Rebecca Willett; David Page

doi:10.1007/978-1-4939-8955-3_15

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

Methods Mol Biol. 2019:1903:255-267. doi: 10.1007/978-1-4939-8955-3_15.

Authors

Zhaobin Kuang¹, Yujia Bao², James Thomson³, Michael Caldwell⁴, Peggy Peissig⁴, Ron Stewart³, Rebecca Willett⁵, David Page⁵

Affiliations

¹ The University of Wisconsin, Madison, WI, USA. zkuang@wisc.edu.
² The Massachusetts Institute of Technology, Cambridge, MA, USA.
³ The Morgridge Institute for Research, Madison, WI, USA.
⁴ The Marshfield Clinic, Marshfield, WI, USA.
⁵ The University of Wisconsin, Madison, WI, USA.

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.

Keywords: Computational drug repurposing; Electronic health records; Longitudinal data; Self-controlled case series; Silico repurposing.

Publication types

Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't
Research Support, U.S. Gov't, Non-P.H.S.

MeSH terms

Algorithms
Computational Biology / methods*
Drug Repositioning / methods*
Electronic Health Records
Humans
Machine Learning*

Abstract

Publication types

MeSH terms

Grants and funding