Abstract
The COVID-19 pandemic led to an unprecedented volume of articles published in scientific journals with possible strategies and technologies to contain the disease. Academic papers summarize the main findings of scientific research, which are vital for decision-making, especially regarding health data. However, due to the technical language used in this type of manuscript, its understanding becomes complex for professionals who do not have a greater affinity with scientific research. Thus, building strategies that improve communication between health professionals and academics is essential. In this paper, we show a semi-automated approach to analyze the scientific literature through natural language processing using as a basis the results collected by the “Scientific Evidence Panel on Pharmacological Treatment and Vaccines – COVID-19” proposed by the Brazilian Ministry of Health. After manual curation, we obtained an accuracy of 0.64, precision of 0.74, recall of 0.70, and F1 score of 0.72 for the analysis of the using-context of technologies, such as treatments or medicines (i.e., we evaluated if the keyword was used in a positive or negative context). Our results demonstrate how machine learning and natural language processing techniques can greatly help understand data from the literature, taking into account the context. Additionally, we present a proposal for a scientific panel called SimplificaSUS, which includes evidence taken from scientific articles evaluated through machine learning and natural language processing methods.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Data Availability
Supplementary material, data, and scripts are available at https://github.com/LBS-UFMG/SimplificaSUS.
References
Hu, B., Guo, H., Zhou, P., Shi, Z.-L.: Characteristics of SARS-CoV-2 and COVID-19. Nat. Rev. Microbiol. 19(3), 141–154 (2021)
Pairo-Castineira, E., et al.: Genetic mechanisms of critical illness in COVID-19. Nature 591(7848) (2021). Art. no 7848. https://doi.org/10.1038/s41586-020-03065-y
Melms, J.C., et al.: A molecular single-cell lung atlas of lethal COVID-19. Nature 595(7865) (2021). Art. no 7865. https://doi.org/10.1038/s41586-021-03569-1
Pathak, G.A., et al.: A first update on mapping the human genetic architecture of COVID-19. Nature 608(7921) (2022). Art. no 7921. https://doi.org/10.1038/s41586-022-04826-7
Dos Santos, V.P., et al.: E-Volve: understanding the impact of mutations in SARS-CoV-2 variants spike protein on antibodies and ACE2 affinity through patterns of chemical interactions at protein interfaces. PeerJ 10, e13099 (2022). https://doi.org/10.7717/peerj.13099
Harper, L., et al.: The impact of COVID-19 on research. J. Pediatr. Urol. 16(5), 715–716 (2020). https://doi.org/10.1016/j.jpurol.2020.07.002
Glasziou, P.P., Sanders, S., Hoffmann, T.: Waste in covid-19 research. BMJ 369, m1847 (2020). https://doi.org/10.1136/bmj.m1847
Fraser, N., et al.: Preprinting the COVID-19 pandemic. bioRxiv, p. 2020.05.22.111294, 5 de fevereiro de 2021. https://doi.org/10.1101/2020.05.22.111294
Painel de Evidências Científicas sobre Tratamento Farmacológico e Vacinas - COVID-19. https://infoms.saude.gov.br/extensions/evidencias_covid/evidencias_covid.html. acesso em 20 de abril de 2023
Li, X., et al.: Is hydroxychloroquine beneficial for COVID-19 patients? Cell Death Dis. 11(7) (2020). Art. no 7. https://doi.org/10.1038/s41419-020-2721-8
Schwartz, I.S., Boulware, D.R., Lee, T.C.: Hydroxychloroquine for COVID19: the curtains close on a comedy of errors. Lancet Reg. Health – Am. 11 (2022). https://doi.org/10.1016/j.lana.2022.100268
Avezum, Á., et al.: Hydroxychloroquine versus placebo in the treatment of non-hospitalised patients with COVID-19 (COPE – Coalition V): a double-blind, multicentre, randomised, controlled trial. Lancet Reg. Health – Am. 11 (2022). https://doi.org/10.1016/j.lana.2022.100243
Maisonnasse, P., et al.: Hydroxychloroquine use against SARS-CoV-2 infection in non-human primates. Nature 585(7826) (2020). Art. no 7826. https://doi.org/10.1038/s41586-020-2558-4
Hoffmann, M., et al.: Chloroquine does not inhibit infection of human lung cells with SARS-CoV-2. Nature 585(7826) (2020). Art. no 7826. https://doi.org/10.1038/s41586-020-2575-3
Dhibar, D.P., et al.: The ‘myth of Hydroxychloroquine (HCQ) as post-exposure prophylaxis (PEP) for the prevention of COVID-19’ is far from reality. Sci. Rep. 13(1) (2023). Art. no 1. https://doi.org/10.1038/s41598-022-26053-w
Ghazy, R.M., et al.: A systematic review and meta-analysis on chloroquine and hydroxychloroquine as monotherapy or combined with azithromycin in COVID-19 treatment. Sci. Rep. 10(1) (2020). Art. no 1. https://doi.org/10.1038/s41598-020-77748-x
Hutto, C., Gilbert, E.: Vader: a parsimonious rule-based model for sentiment analysis of social media text. apresentado em Proceedings of the International AAAI Conference on Web and Social Media, pp. 216–225 (2014)
Abubakar, A.R., et al.: Systematic review on the therapeutic options for COVID-19: clinical evidence of drug efficacy and implications. Infect. Drug Resist., 4673–4695 (2020)
Rahmani, H., et al.: Comparing outcomes of hospitalized patients with moderate and severe COVID-19 following treatment with hydroxychloroquine plus atazanavir/ritonavir. DARU J. Pharm. Sci. 28(2), 625–634 (2020). https://doi.org/10.1007/s40199-020-00369-2
Gomez-Mayordomo, V., Montero-Escribano, P., Matías-Guiu, J.A., González-García, N., Porta-Etessam, J., Matías-Guiu, J.: Clinical exacerbation of SARS-CoV2 infection after fingolimod withdrawal. J. Med. Virol. 93(1), 546–549 (2021). https://doi.org/10.1002/jmv.26279
Kim, Y.C., Dema, B., Reyes-Sandoval, A.: COVID-19 vaccines: breaking record times to first-in-human trials. NPJ Vaccines 5(1), 34 (2020)
Karadeniz, H., Yamak, B.A., Özger, H.S., Sezenöz, B., Tufan, A., Emmi, G.: Anakinra for the treatment of COVID-19-associated pericarditis: a case report. Cardiovasc. Drugs Ther. 34(6), 883–885 (2020). https://doi.org/10.1007/s10557-020-07044-3
Davis, M.R., McCreary, E.K., Pogue, J.M.: That escalated quickly: Remdesivir’s place in therapy for COVID-19. Infect. Dis. Ther. 9(3), 525–536 (2020). https://doi.org/10.1007/s40121-020-00318-1
Roustit, M., Guilhaumou, R., Molimard, M., Drici, M.-D., Laporte, S., Montastruc, J.-L.: Chloroquine and hydroxychloroquine in the management of COVID-19: much kerfuffle but little evidence. Therapies 75(4), 363–370 (2020). https://doi.org/10.1016/j.therap.2020.05.010
La Rosée, F., et al.: The Janus kinase 1/2 inhibitor ruxolitinib in COVID-19 with severe systemic hyperinflammation. Leukemia 34(7) (2020). . Art. no 7. https://doi.org/10.1038/s41375-020-0891-0
Acknowledgments
The authors would like to thank Mariana Parise for her valuable contributions during the discussions on the elaboration of this manuscript. The authors also thank the funding agencies: CAPES, CNPq, and FAPEMIG. We also thank the Campus Party, Brazilian Ministry of Health, Fiocruz, and DECIT teams.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Carvalho, F. et al. (2023). Using Natural Language Processing for Context Identification in COVID-19 Literature. In: Reis, M.S., de Melo-Minardi, R.C. (eds) Advances in Bioinformatics and Computational Biology. BSB 2023. Lecture Notes in Computer Science(), vol 13954. Springer, Cham. https://doi.org/10.1007/978-3-031-42715-2_7
Download citation
DOI: https://doi.org/10.1007/978-3-031-42715-2_7
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-42714-5
Online ISBN: 978-3-031-42715-2
eBook Packages: Computer ScienceComputer Science (R0)