Using Natural Language Processing for Context Identification in COVID-19 Literature

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 howmachine 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.