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

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

Author information

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

PMID:
30547447
PMCID:
PMC6296259
DOI:
10.1007/978-1-4939-8955-3_15
[Indexed for MEDLINE]
Free PMC Article

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