A Process for Extracting Data from Social Media to Detect Adverse Drug Reactions
Abstract
Pharmacovigilance is an area responsible for, among many topics, detecting adverse drug reactions (ADRs). Such reactions can be reported in systems designed for this purpose. However, social media such as Twitter can reveal various notifications. This work aims to present a process for extracting data from social media to support the detection of ADRs. This work could help future research related to analyzing textual data extracted from social media to detect ADRs for the benefit of Brazilian pharmacovigilance
Keywords:
Pharmacovigilance, Adverse Reactions, Social Networks, Data Extraction
References
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Zhang, Y., Cui, S., and Gao, H. (2020). Adverse drug reaction detection on social media with deep linguistic features. Journal of Biomedical Informatics, 106. cited By 4.
Anvisa (2020). Portal brasileiro de dados abertos: Medicamentos registrados no brasil. https://dados.gov.br/dataset/medicamentos-registrados-no-brasil. Acessado em: Setembro/2021.
CNS (1997). Regulamento para a realização de ensaios clínicos com medicamentos no brasil. https://bvsms.saude.gov.br/bvs/saudelegis/cns/1997/res0251_07_08_1997.html. Acessado em: abril/2021.
Duval, F. V. and da Silva, F. A. B. (2019). Mining in twitter for adverse events from malaria drugs: The case of doxycycline. Cadernos de Saude Publica, 35(5).
Ferreira, J., Miranda, M., Abelha, A., and Machado, J. (2010). O Processo ETL em Sistemas DataWarehouse. INForum 2010 - II Simpósio de Informática, pages 757–765.
Koutkias, V., Lillo-Le Louët, A., and Jaulent, M.-C. (2017). Exploiting heterogeneous publicly available data sources for drug safety surveillance: computational framework and case studies. Expert Opinion on Drug Safety, 16(2):113–124.
Lombardino, J. G. and Lowe, J. A. (2004). The role of the medicinal chemist in drug discovery—then and now. Nature Reviews Drug Discovery, 3(10):853–862.
Organization, W. H. et al. (2002). The importance of pharmacovigilance.
Pirmohamed, M., Breckenridge, A. M., Kitteringham, N. R., and Park, B. K. (1998). Adverse drug reactions. Bmj, 316(7140):1295–1298.
Plachouras, V., Leidner, J. L., and Garrow, A. G. (2016). Quantifying self-reported adverse drug events on Twitter: Signal and topic analysis. In ACM International Conference Proceeding Series.
Saff, R. (2018). Epidemiology of Drug Allergy. cited By 1.
TwitterDev (?). Filtered stream:how to build a rule. [link]. Acessado em: setembro/2021.
Vogler, M., Conesa, H. R., de Araújo Ferreira, K., Cruz, F. M., Gasparotto, F. S., Fleck, K., Rebelo, F. M., Kollross, B., and Gonçalves, Y. S. (2020). Electronic reporting systems in pharmacovigilance: The implementation of vigiflow in brazil. Pharmaceutical medicine, 34(5):327–334.
Zhang, Y., Cui, S., and Gao, H. (2020). Adverse drug reaction detection on social media with deep linguistic features. Journal of Biomedical Informatics, 106. cited By 4.
Published
2021-11-23
How to Cite
NASCIMENTO, Luan; CARVALHO, Lucas; SOUZA, Matheus; MAURO, Renato; BELLOZE, Kele.
A Process for Extracting Data from Social Media to Detect Adverse Drug Reactions. In: REGIONAL SCHOOL ON INFORMATICS OF RIO DE JANEIRO (ERI-RJ), 4. , 2021, Online.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2021
.
p. 17-24.
DOI: https://doi.org/10.5753/eri-rj.2021.18770.
