Key points are not available for this paper at this time.
This study aims to present a methodology to identify, through Twitter posts, predefined symptoms of COVID-19 aided by Deep Learning techniques, namely Transformers Networks. The proposed approach was evaluated on a public Twitter database in Brazilian Portuguese, using user reports of COVID-19 symptoms. We mine the Twitter database, extract phrases with symptoms, compare distributions, and build a database to construct high accuracy Deep Learning networks, which can be used to identify symptoms. We use a crossvalidation procedure to evaluate the result’s performance. Additionally, we interpret the results using a Local Interpretable Model-Agnostic Explanations (LIME) algorithm. We identified 907 tweets containing one or more of the 14 previously chosen COVID-19 symptoms. The most frequently reported symptoms were a cough (392), headache (154), runny nose (143), fever (124), nausea (106), and diarrhea (105) amongst users who reported at least one symptom. The BERT architecture identified all 14 symptoms reported in Twitter phrases in Portuguese, resulting in identifying each symptom with over 97% accuracy and over 0.95 of AUC-ROC at the test dataset. This project is a step towards a complementary tool to identify symptoms in future automated clinical settings, e.g., medical chatbots, to support faster clinical assessment in Portuguese.
Machado et al. (Fri,) studied this question.