Literature DB >> 30547447

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

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

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.

Entities:  

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

Mesh:

Year:  2019        PMID: 30547447      PMCID: PMC6296259          DOI: 10.1007/978-1-4939-8955-3_15

Source DB:  PubMed          Journal:  Methods Mol Biol        ISSN: 1064-3745


  14 in total

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7.  Baseline Regularization for Computational Drug Repositioning with Longitudinal Observational Data.

Authors:  Zhaobin Kuang; James Thomson; Michael Caldwell; Peggy Peissig; Ron Stewart; David Page
Journal:  IJCAI (U S)       Date:  2016-07

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6.  Evolving scenario of big data and Artificial Intelligence (AI) in drug discovery.

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7.  Framework for identifying drug repurposing candidates from observational healthcare data.

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  7 in total

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