An Application of Recurrent Neural Networks for Estimating the Prognosis of COVID-19 Patients in Northern Italy
Published in Proceedings of the 19th International Conference on Artificial Intelligence in Medicine (AIME), 2021
Hospital overloads and limited healthcare resources (ICU beds, ventilators, etc.) are fundamental issues related to the outbreak of the COVID-19 pandemic. Machine learning techniques can help the hospitals to recognise in advance the patients at risk of death, and consequently to allocate their resources in a more efficient way. In this paper we present a tool based on Recurrent Neural Networks to predict the risk of death for hospitalised patients with COVID-19. The features used in our predictive models consist of demographics information, several laboratory tests, and a score that indicates the severity of the pulmonary damage observed by chest X-ray exams. The networks were trained and tested using data of 2000 patients hospitalised in Lombardy, the region most affected by COVID-19 in Italy. The experimental results show good performance in solving the addressed task. paper link
Recommended citation: An Application of Recurrent Neural Networks for Estimating the Prognosis of COVID-19 Patients in Northern Italy, M. Chiari, AE. Gerevini, M. Olivato, L. Putelli, N. Rossetti, I. Serina - Proceedings of the 19th International Conference on Artificial Intelligence in Medicine (AIME), 2021