| Journal: | Computación y sistemas |
| Database: | PERIÓDICA |
| System number: | 000408044 |
| ISSN: | 1405-5546 |
| Authors: | Moreno-García, Carlos Francisco1 Aceves-Mains, Magaly1 Serratosa, Francesc1 |
| Institutions: | 1Universidad Rovira i Virgili, Tarragona. España |
| Year: | 2016 |
| Season: | Ene-Mar |
| Volumen: | 20 |
| Number: | 1 |
| Pages: | 7-17 |
| Country: | México |
| Language: | Inglés |
| Document type: | Artículo |
| Approach: | Experimental, aplicado |
| English 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 |
| Disciplines: | Ciencias de la computación, Matemáticas |
| Keyword: | Inteligencia artificial, Matemáticas aplicadas, Aprendizaje automático, Meta-análisis, Investigación médica, Revisiones sistemáticas, Estadística, Algoritmos |
| Keyword: | Computer science, Mathematics, Artificial intelligence, Applied mathematics, Machine learning, Meta-analysis, Medical research, Systematic reviews, Statistics, Algorithms |
| Full text: | Texto completo (Ver PDF) |
