@inproceedings{coelho-da-silva-etal-2023-tracking,
    title = "Tracking the Evolution of Covid-19 Symptoms through Clinical Conversations",
    author = "Coelho Da Silva, Ticiana  and
      Fernandes De Mac{\^e}do, Jos{\'e}  and
      Magalh{\~a}es, R{\'e}gis",
    editor = "Naumann, Tristan  and
      Ben Abacha, Asma  and
      Bethard, Steven  and
      Roberts, Kirk  and
      Rumshisky, Anna",
    booktitle = "Proceedings of the 5th Clinical Natural Language Processing Workshop",
    month = jul,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.clinicalnlp-1.6",
    doi = "10.18653/v1/2023.clinicalnlp-1.6",
    pages = "41--47",
    abstract = "The Coronavirus pandemic has heightened the demand for technological solutions capable of gathering and monitoring data automatically, quickly, and securely. To achieve this need, the Plant{\~a}o Coronavirus chatbot has been made available to the population of Cear{\'a} State in Brazil. This chatbot employs automated symptom detection technology through Natural Language Processing (NLP). The proposal of this work is a symptom tracker, which is a neural network that processes texts and captures symptoms in messages exchanged between citizens of the state and the Plant{\~a}o Coronavirus nurse/doctor, i.e., clinical conversations. The model has the ability to recognize new patterns and has identified a high incidence of altered psychological behaviors, including anguish, anxiety, and sadness, among users who tested positive or negative for Covid-19. As a result, the tool has emphasized the importance of expanding coverage through community mental health services in the state.",
}

The Coronavirus pandemic has heightened the demand for technological solutions capable of gathering and monitoring data automatically, quickly, and securely. To achieve this need, the Plant{\~a}o Coronavirus chatbot has been made available to the population of Cear{\'a} State in Brazil. This chatbot employs automated symptom detection technology through Natural Language Processing (NLP). The proposal of this work is a symptom tracker, which is a neural network that processes texts and captures symptoms in messages exchanged between citizens of the state and the Plant{\~a}o Coronavirus nurse/doctor, i.e., clinical conversations. The model has the ability to recognize new patterns and has identified a high incidence of altered psychological behaviors, including anguish, anxiety, and sadness, among users who tested positive or negative for Covid-19. As a result, the tool has emphasized the importance of expanding coverage through community mental health services in the state.
Coelho Da Silva, Ticiana Fern es De Mac{\^e}do, Jos{\'e} Magalh{\~a}es, R{\'e}gis
Tracking the Evolution of Covid-19 Symptoms through Clinical Conversations