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.
An Application of Recurrent Neural Networks for Estimating the Prognosis of COVID-19 Patients in Northern Italy / Chiari, M.; Gerevini, A. E.; Olivato, M.; Putelli, L.; Rossetti, N.; Serina, I.. - 12721:(2021), pp. 318-328. (Intervento presentato al convegno 19th International Conference on Artificial Intelligence in Medicine, AIME 2021 tenutosi a AIME 2021) [10.1007/978-3-030-77211-6_36].
An Application of Recurrent Neural Networks for Estimating the Prognosis of COVID-19 Patients in Northern Italy
Gerevini A. E.;Rossetti N.;
2021
Abstract
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.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
