文章基本信息

标题：Unsupervised Machine Learning Application to Perform a Systematic Review and Meta-Analysis in Medical Research
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作者：Carlos Francisco Moreno-García ; Magaly Aceves-Martins ; Francesc Serratosa 等
期刊名称：Computación y Sistemas
印刷版ISSN：1405-5546
出版年度：2016
卷号：20
期号：1
页码：7-17
语种：English
出版社：Instituto Politécnico Nacional
其他摘要：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 syste matic review and the meta - analysis. The s e technique s 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 th e classification. I n this paper, we use unsupervised machine learning methodologies to describe a simple yet effective algorithm that , given a dataset with missing data, complete s such data, which lead s to a mo re 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.
其他关键词：Systematic review; meta-analysis; unsupervised machine learning; recommender systems; principal component analysis.