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Revista de la Facultad de Medicina

Print version ISSN 0120-0011

Abstract

SANCHEZ-DUQUE, Jorge Andrés; GAVIRIA-MENDOZA, Andrés; MORENO-GUTIERREZ, Paula Andrea  and  MACHADO-ALBA, Jorge Enrique. Big data, pharmacoepidemiology and pharmacovigilance. rev.fac.med. [online]. 2020, vol.68, n.1, pp.117-120. ISSN 0120-0011.  http://dx.doi.org/10.15446/revfacmed.v68n1.73456.

Big data is a term that comprises a group of technological tools capable of processing extremely large heterogeneous data sets, which are continuously collected and are available to be used at any time, and, therefore, constitutes a source of scientific evidence production.

In the pharmacoepidemiology field, analyses made using these data sets may result in the development of pharmacological therapies that are more efficient, less expensive, and have a lower occurrence rate of adverse reactions. Likewise, the use of tools such as Text Mining or Machine Learning has led to major advances in pharmacoepidemiology and pharmacovigilance areas, so it is likely that these tools will be increasingly used over time.

Keywords : Artificial Intelligence; Automatic Data Processing; Data Accuracy; Data Mining; Machine Learning; Registries (MeSH).

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