Authors:
Felipe Duval
;
Ernesto Caffarena
;
Oswaldo Cruz
and
Fabrício Silva
Affiliation:
Fundação Oswaldo Cruz, Brazil
Keyword(s):
Adverse Event, Data Mining, Text Mining, Big Data, Pharmacovigilance, Neglected Diseases, UMLS, Twitter, Tweet, Nosql, Disproportionality Analysis, Malaria, Dengue, Ruby, Ctakes, Drug Safety, Adverse Drug Reaction, Natural Language Processor, Post-Marketing Phase, REST API.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
BioInformatics & Pattern Discovery
;
Knowledge Discovery and Information Retrieval
;
Knowledge-Based Systems
;
Mining Text and Semi-Structured Data
;
Soft Computing
;
Symbolic Systems
;
Web Mining
Abstract:
At the post-marketing phase when drugs are used by large populations and for long periods, unexpected adverse events may occur altering the risk-benefit relation of drugs, sometimes requiring a regulatory action. These events at the post-marketing phase require a significant increase in health care since they result in unnecessary damage, often fatal, to patients. Therefore, the early discovery of adverse events in the post-marketing phase is a primary goal of the health system, in particular for pharmacovigilance systems. The main purpose of this paper is to prove that Twitter can be used as a source to find new and already known adverse drug events. This proposal has a prominent social relevance, as it will help pharmacovigilance systems.