Resumen

MORENO-GARCIA, Carlos Francisco; ACEVES-MARTINS, Magaly  y  SERRATOSA, Francesc. Unsupervised Machine Learning Application to Perform a Systematic Review and Meta-Analysis in Medical Research. Comp. y Sist. [online]. 2016, vol.20, n.1, pp.7-17. ISSN 2007-9737.  https://doi.org/10.13053/cys-20-1-2360.

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.

Palabras llave : Systematic review; meta-analysis; unsupervised machine learning; recommender systems; principal component analysis.

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