Assessment of the evolution of patients hospitalized for COVID-19 in Paraná
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Abstract
The COVID-19 pandemic was marked by great fear, as it was a new disease of which we had no knowledge of its effects and prevention methods. However, during this period,we also made significant advances in research across various fields, from studying the causes and effects of the disease to the development of vaccines. In this study, we focus on assessing the evolution (recovery/death) of COVID-19 inpatients, who were hospitalized in the state of Paraná, Brazil in the year of 2022. To achieve this, we analyzed data from the System of Epidemiological Surveillance of Influenza (SIVEP) which provides information about Brazilian patients hospitalized with severe acute respiratory syndrome, using several machine learning techniques that allowed us to relate the patient evolution to possible associated factors. Results showed that age, gender, education, and neurological disorder, among other factors, have significant impacts in the inpatients evolution. When predicting the patient outcome, we obtained an accuracy over 75%, which shows the efficiency of the models.
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