Unsupervised Machine Learning Application to Perform a Systematic Review and Meta-Analysis in Medical Research
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| Format: | Artículo científico |
| Langue: | en |
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Instituto Politécnico Nacional
2016
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| _version_ | 1876480186075054080 |
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| author | Carlos Francisco Moreno-García |
| author_facet | Carlos Francisco Moreno-García |
| contents | Unsupervised Machine Learning Application to Perform a Systematic Review and Meta-Analysis in Medical Research Carlos Francisco Moreno-García Magaly Aceves-Martins Francesc Serratosa Computación meta analysis Systematic review recommender systems principal component analysis 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. 2016 artículo científico 1405-5546 https://www.redalyc.org/articulo.oa?id=61544821002 en http://www.redalyc.org/revista.oa?id=615 Computación y Sistemas application/pdf Instituto Politécnico Nacional Computación y Sistemas (México) Num.1 Vol.20 |
| format | Artículo científico |
| id | redalyc_61544821002 |
| institution | Redalyc |
| language | en |
| publishDate | 2016 |
| publisher | Instituto Politécnico Nacional |
| spellingShingle | Unsupervised Machine Learning Application to Perform a Systematic Review and Meta-Analysis in Medical Research Carlos Francisco Moreno-García Computación meta analysis Systematic review recommender systems principal component analysis Unsupervised Machine Learning Application to Perform a Systematic Review and Meta-Analysis in Medical Research Carlos Francisco Moreno-García Magaly Aceves-Martins Francesc Serratosa Computación meta analysis Systematic review recommender systems principal component analysis 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. 2016 artículo científico 1405-5546 https://www.redalyc.org/articulo.oa?id=61544821002 en http://www.redalyc.org/revista.oa?id=615 Computación y Sistemas application/pdf Instituto Politécnico Nacional Computación y Sistemas (México) Num.1 Vol.20 |
| title | Unsupervised Machine Learning Application to Perform a Systematic Review and Meta-Analysis in Medical Research |
| topic | Computación meta analysis Systematic review recommender systems principal component analysis |
| url | https://www.redalyc.org/articulo.oa?id=61544821002 |