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Unsupervised machine learning application to perform a systematic review and meta-analysis in medical research.

Moreno-Garc�a, Carlos Francisco; Aceves-Martins, Magaly; Serratosa, Francesc

Authors

Magaly Aceves-Martins

Francesc Serratosa



Abstract

When trying to synthesize information from multiple sources and perform a statistical review to compare them, particularly in the medical research field, several statistical tools are available, most common are the systematic review and the meta-analysis. These techniques allow the comparison of the effectiveness or success among a group of studies. However, a problem of these tools is that if the information to be compared is incomplete or mismatched between two or more studies, the comparison becomes an arduous task. On a parallel line, machine learning methodologies have been proven to be a reliable resource, such software is developed to classify several variables and learn from previous experiences to improve the classification. In this paper, we use unsupervised machine learning methodologies to describe a simple yet effective algorithm that, given a dataset with missing data, completes such data, which leads to a more complete systematic review and meta-analysis, capable of presenting a final effectiveness or success rating between studies. Our method is first validated in a movie ranking database scenario, and then used in a real life systematic review and meta-analysis of obesity prevention scientific papers, where 66.6% of the outcomes are missing.

Citation

MORENO-GARCÍA, C.F., ACEVES-MARTINS, M. and SERRATOSA, F. 2016. Unsupervised machine learning application to perform a systematic review and meta-analysis in medical research. Computación y sistemas [online], 20(1), pages 7-17. Available from: https://doi.org/10.13053/CyS-20-1-2360

Journal Article Type Article
Acceptance Date Jan 1, 2016
Online Publication Date Mar 31, 2016
Publication Date Mar 31, 2016
Deposit Date Oct 17, 2020
Publicly Available Date Oct 27, 2020
Journal Computación y Sistemas
Print ISSN 1405-5546
Electronic ISSN 2007-9737
Publisher Instituto Politécnico Nacional. Centro de Investigación en Computación
Peer Reviewed Peer Reviewed
Volume 20
Issue 1
Pages 7-17
DOI https://doi.org/10.13053/CyS-20-1-2360
Keywords Systematic review; Meta-analysis; Unsupervised machine learning; Recommender systems; Principal component analysis
Public URL https://rgu-repository.worktribe.com/output/977249

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