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

Moreno-García, Carlos Francisco; Aceves-Martins, Magaly; Serratosa, Francesc

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: Review
• 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