O uso de análise de sentimentos sobre mídia social digital para extração de reações adversas a medicamentos devido à interação medicamentosa durante um intervalo de tempo
Resumo
A utilização de mídia social digital (MSD) para a extração de reações adversas a medicamentos (RAM) vem alcançando progressos nos últimos anos. Esse trabalho realizará um estudo longitudinal, colhendo tweets e outros dados de MSD durante vinte meses, sendo esperado a formação de um corpus com 200 mil opinões sobre RAM. A pesquisa objetiva descobrir RAM em MSD por meio do uso de análise de sentimento a partir de reações provocadas por interação medicamentosa em um intervalo de tempo. Serão desenvolvidos experimentos de text mining e classificadores, possivelmente agrupados em comitê (ensemble learning) envolvendo estudos com redes neurais em aprendizado profundo semi-supervisionado. Os resultados esperados são a descoberta desse tipo de RAM subnotificada e que não estejam registradas nas bases de dados oficiais da FAERS FDA Adverse Event Reporting System - Sistema de Relatórios de Eventos Adversos da FDA.
Referências
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CALIX, R. A. et al. Deep gramulator: Improving precision in the classification of personal health-experience tweets with deep learning. In: 2017 IEEE International Conference on Bioinformatics and Biomedicine (BIBM 2017. p. 1154 – 1159.
COMFORT, S. et al. Sorting Through the Safety Data Haystack: Using Machine Learning to Identify Individual Case Safety Reports in Social-Digital Media. Drug Safety, 2018 .
DEV, S. et al. Automated classification of adverse events in pharmacovigilance. In: 2017 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). [S.l.]: IEEE, 2017. p. 905 – 909.
HUANG, J. et al. Comparing Different Adverse Effects Among Multiple Drugs Using FAERS Data. Studies in health technology and informatics, v. 245, 2017.
HUYNH, T. et al. Adverse Drug Reaction Classification With Deep Neural Networks. In: Proceedings of COLING 2016: Technical papers. COLING, 2016. p. 877 – 887.
LIU, J.; ZHAO, S.; WANG, G. SSEL-ADE: A semi-supervised ensemble learning framework for extracting adverse drug events from social media. Artificial Intelligence in Medicine, v. 84, p. 34 – 49, 2018. ISSN 0933-3657
NIKFARJAM, A. et al. Pharmacovigilance from social media: mining adverse drug reaction mentions using sequence labeling with word embedding cluster features. J Am Med Inform Assoc, v. 22, 2015.
PAPPA, D.; STERGIOULAS, L. K. Harnessing social media data for pharmacovigilance: a review of current state of the art, challenges and future directions. International Journal of Data Science and Analytics, feb 2019. ISSN 2364-4168.
PENG, Y.; MOH, M.; MOH, T. Efficient Adverse Drug Event Extraction Using Twitter Sentiment Analysis. In: Proceedings of the 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining. Piscataway, NJ, USA: IEEE Press, 2016.
RAJAPAKSHA, P.; WEERASINGHE, R. Identifying Adverse Drug Reactions by analyzing Twitter messages. In: 2015 Fifteenth International Conference on Advances in ICT for Emerging Regions (ICTer). [S.l.: s.n.], 2015. p. 37 – 42.
SARKER, A. et al. Utilizing social media data for pharmacovigilance: a review. J Biomed Inform, v. 54, 2015. Disponível em: https://doi.org/10.1016/j.jbi.2015.02.004.
SONG, Q.; LI, B.; XU, Y. Research on Adverse Drug Reaction Recognitions Based on Conditional Random Field. In: . [S.l.: s.n.], 2017. p. 97 – 101.
WONG, A. et al. Natural language processing and its implications for the future of medication safety: a narrative review of recent advances and challenges. Pharmacotherapy: The Journal of Human Pharmacology and Drug Therapy, v. 38, n. 8, p. 822 – 841, 2018.
WUNNAVA, S. et al. Bidirectional LSTM-CRF for adverse drug event tagging in electronic health records. In: International Workshop on Medication and Adverse Drug Event Detection. [S.l.: s.n.], 2018. p. 48 – 56.
YANG, X.; BIAN, J.; WU, Y. Detecting medications and adverse drug events in clinical notes using recurrent neural networks. In: International workshop on medication and adverse drug event detection. [S.l.: s.n.], 2018. p. 1 – 6.
YUWEN, L.; CHEN, S.; ZHANG, H. Detecting Potential Serious Adverse Drug Reactions Using Sequential Pattern Mining Method. In: 2018 IEEE 9th International Conference on Software Engineering and Service Science (ICSESS). [S.l.: s.n.], 2018. p. 56 – 59.
ZOLNOORI, M. et al. A systematic approach for developing a corpus of patient reported adverse drug events: A case study for SSRI and SNRI medications. Journal of Biomedical Informatics, v. 90, 2019. ISSN 1532-0464.
BANDA, J. M. et al. A curated and standardized adverse drug event resource to accelerate drug safety research. Scientific data, v. 3, p. 160026 – 160026, may 2016.
CALIX, R. A. et al. Deep gramulator: Improving precision in the classification of personal health-experience tweets with deep learning. In: 2017 IEEE International Conference on Bioinformatics and Biomedicine (BIBM 2017. p. 1154 – 1159.
COMFORT, S. et al. Sorting Through the Safety Data Haystack: Using Machine Learning to Identify Individual Case Safety Reports in Social-Digital Media. Drug Safety, 2018 .
DEV, S. et al. Automated classification of adverse events in pharmacovigilance. In: 2017 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). [S.l.]: IEEE, 2017. p. 905 – 909.
HUANG, J. et al. Comparing Different Adverse Effects Among Multiple Drugs Using FAERS Data. Studies in health technology and informatics, v. 245, 2017.
HUYNH, T. et al. Adverse Drug Reaction Classification With Deep Neural Networks. In: Proceedings of COLING 2016: Technical papers. COLING, 2016. p. 877 – 887.
LIU, J.; ZHAO, S.; WANG, G. SSEL-ADE: A semi-supervised ensemble learning framework for extracting adverse drug events from social media. Artificial Intelligence in Medicine, v. 84, p. 34 – 49, 2018. ISSN 0933-3657
NIKFARJAM, A. et al. Pharmacovigilance from social media: mining adverse drug reaction mentions using sequence labeling with word embedding cluster features. J Am Med Inform Assoc, v. 22, 2015.
PAPPA, D.; STERGIOULAS, L. K. Harnessing social media data for pharmacovigilance: a review of current state of the art, challenges and future directions. International Journal of Data Science and Analytics, feb 2019. ISSN 2364-4168.
PENG, Y.; MOH, M.; MOH, T. Efficient Adverse Drug Event Extraction Using Twitter Sentiment Analysis. In: Proceedings of the 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining. Piscataway, NJ, USA: IEEE Press, 2016.
RAJAPAKSHA, P.; WEERASINGHE, R. Identifying Adverse Drug Reactions by analyzing Twitter messages. In: 2015 Fifteenth International Conference on Advances in ICT for Emerging Regions (ICTer). [S.l.: s.n.], 2015. p. 37 – 42.
SARKER, A. et al. Utilizing social media data for pharmacovigilance: a review. J Biomed Inform, v. 54, 2015. Disponível em: https://doi.org/10.1016/j.jbi.2015.02.004.
SONG, Q.; LI, B.; XU, Y. Research on Adverse Drug Reaction Recognitions Based on Conditional Random Field. In: . [S.l.: s.n.], 2017. p. 97 – 101.
WONG, A. et al. Natural language processing and its implications for the future of medication safety: a narrative review of recent advances and challenges. Pharmacotherapy: The Journal of Human Pharmacology and Drug Therapy, v. 38, n. 8, p. 822 – 841, 2018.
WUNNAVA, S. et al. Bidirectional LSTM-CRF for adverse drug event tagging in electronic health records. In: International Workshop on Medication and Adverse Drug Event Detection. [S.l.: s.n.], 2018. p. 48 – 56.
YANG, X.; BIAN, J.; WU, Y. Detecting medications and adverse drug events in clinical notes using recurrent neural networks. In: International workshop on medication and adverse drug event detection. [S.l.: s.n.], 2018. p. 1 – 6.
YUWEN, L.; CHEN, S.; ZHANG, H. Detecting Potential Serious Adverse Drug Reactions Using Sequential Pattern Mining Method. In: 2018 IEEE 9th International Conference on Software Engineering and Service Science (ICSESS). [S.l.: s.n.], 2018. p. 56 – 59.
ZOLNOORI, M. et al. A systematic approach for developing a corpus of patient reported adverse drug events: A case study for SSRI and SNRI medications. Journal of Biomedical Informatics, v. 90, 2019. ISSN 1532-0464.
Publicado
03/10/2019
Como Citar
RIBEIRO, Luiz; GARCIA, Ana Cristina.
O uso de análise de sentimentos sobre mídia social digital para extração de reações adversas a medicamentos devido à interação medicamentosa durante um intervalo de tempo. In: DESENHO DE PESQUISA - SIMPÓSIO BRASILEIRO DE SISTEMAS COLABORATIVOS (SBSC), 15. , 2019, Rio de Janeiro.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2019
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p. 7-12.
