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Authors: Felipe Duval ; Ernesto Caffarena ; Oswaldo Cruz and Fabrício Silva

Affiliation: Fundação Oswaldo Cruz, Brazil

ISBN: 978-989-758-048-2

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.

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Paper citation in several formats:
Duval, F.; Caffarena, E.; Cruz, O. and Silva, F. (2014). Mining for Adverse Drug Events on Twitter.In Proceedings of the International Conference on Knowledge Discovery and Information Retrieval - Volume 1: KDIR, (IC3K 2014) ISBN 978-989-758-048-2, pages 354-359. DOI: 10.5220/0005135203540359

@conference{kdir14,
author={Felipe Duval. and Ernesto Caffarena. and Oswaldo Cruz. and Fabrício Silva.},
title={Mining for Adverse Drug Events on Twitter},
booktitle={Proceedings of the International Conference on Knowledge Discovery and Information Retrieval - Volume 1: KDIR, (IC3K 2014)},
year={2014},
pages={354-359},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005135203540359},
isbn={978-989-758-048-2},
}

TY - CONF

JO - Proceedings of the International Conference on Knowledge Discovery and Information Retrieval - Volume 1: KDIR, (IC3K 2014)
TI - Mining for Adverse Drug Events on Twitter
SN - 978-989-758-048-2
AU - Duval, F.
AU - Caffarena, E.
AU - Cruz, O.
AU - Silva, F.
PY - 2014
SP - 354
EP - 359
DO - 10.5220/0005135203540359

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