Recognizing pharmacovigilance named entities in Brazilian Portuguese with CoreNLP
Resumo
Textual data sources may assist in the detection of adverse events not predicted for a particular drug. However, given the amount of information available in several sources, it is reasonable to adopt a computational approach to analyze these sources to search for adverse events. In this scenario, we created an extension of CoreNLP to process Brazilian Portuguese texts from pharma- covigilance area. We trained three natural language models: a Part-of-speech tagger, a parser and a Named Entity Recognizer. Preliminary results indicate success in generating a dependency tree for phrases in the pharmacovigilance area and in identifying pharmacovigilance named entities.
Palavras-chave:
named entity recognition, pharmacovigilance, Brazilian Portuguese
Referências
Benton, A., Ungar, L., Hill, S., Hennessy, S., Mao, J., Chung, A., Leonard, C. E., and Holmes, J. H. (2011). Identifying potential adverse effects using the web: A new approach to medical hypothesis generation. Journal of biomedical informatics, 44(6):989–996.
Chowdhury, G. G. (2003). Natural language processing. Annual review of information science and technology, 37(1):51–89.
Cocos, A., Fiks, A. G., and Masino, A. J. (2017). Deep learning for pharmacovigilance: recurrent neural network architectures for labeling adverse drug reactions in twitter posts. Journal of the American Medical Informatics Association, 24(4):813–821.
Gurulingappa, H., Mateen-Rajpu, A., and Toldo, L. (2012). Extraction of potential ad- verse drug events from medical case reports. Journal of biomedical semantics, 3(1):15.
Hartmann, N., Fonseca, E., Shulby, C., Treviso, M., Rodrigues, J., and Aluisio, S. (2017). Portuguese word embeddings: Evaluating on word analogies and natural language tasks. arXiv preprint arXiv:1708.06025.
Manning, C., Surdeanu, M., Bauer, J., Finkel, J., Bethard, S., and McClosky, D. (2014). The stanford corenlp natural language processing toolkit. In Proceedings of 52nd an- nual meeting of the association for computational linguistics: system demonstrations, pages 55–60.
Nikfarjam, A., Sarker, A., O’Connor, K., Ginn, R., and Gonzalez, G. (2015). Pharma- covigilance from social media: mining adverse drug reaction mentions using sequence labeling with word embedding cluster features. Journal of the American Medical In- formatics Association, 22(3):671–681.
O’Connor, K., Pimpalkhute, P., Nikfarjam, A., Ginn, R., Smith, K. L., and Gonzalez, G. (2014). Pharmacovigilance on twitter? mining tweets for adverse drug reactions. In AMIA annual symposium proceedings, volume 2014, page 924. American Medical Informatics Association.
Chowdhury, G. G. (2003). Natural language processing. Annual review of information science and technology, 37(1):51–89.
Cocos, A., Fiks, A. G., and Masino, A. J. (2017). Deep learning for pharmacovigilance: recurrent neural network architectures for labeling adverse drug reactions in twitter posts. Journal of the American Medical Informatics Association, 24(4):813–821.
Gurulingappa, H., Mateen-Rajpu, A., and Toldo, L. (2012). Extraction of potential ad- verse drug events from medical case reports. Journal of biomedical semantics, 3(1):15.
Hartmann, N., Fonseca, E., Shulby, C., Treviso, M., Rodrigues, J., and Aluisio, S. (2017). Portuguese word embeddings: Evaluating on word analogies and natural language tasks. arXiv preprint arXiv:1708.06025.
Manning, C., Surdeanu, M., Bauer, J., Finkel, J., Bethard, S., and McClosky, D. (2014). The stanford corenlp natural language processing toolkit. In Proceedings of 52nd an- nual meeting of the association for computational linguistics: system demonstrations, pages 55–60.
Nikfarjam, A., Sarker, A., O’Connor, K., Ginn, R., and Gonzalez, G. (2015). Pharma- covigilance from social media: mining adverse drug reaction mentions using sequence labeling with word embedding cluster features. Journal of the American Medical In- formatics Association, 22(3):671–681.
O’Connor, K., Pimpalkhute, P., Nikfarjam, A., Ginn, R., Smith, K. L., and Gonzalez, G. (2014). Pharmacovigilance on twitter? mining tweets for adverse drug reactions. In AMIA annual symposium proceedings, volume 2014, page 924. American Medical Informatics Association.
Publicado
24/06/2019
Como Citar
CUNHA, Alexandre M. R.; BELLOZE, Kele T.; GUEDES, Gustavo P..
Recognizing pharmacovigilance named entities in Brazilian Portuguese with CoreNLP. In: BRAZILIAN E-SCIENCE WORKSHOP (BRESCI), 13. , 2019, Belém.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2019
.
p. 76-79.
ISSN 2763-8774.
DOI: https://doi.org/10.5753/bresci.2019.6314.
