Most ML-based applications for COVID-19 assess the general conditions of a patient trained and tested on cohorts of patients collected over a short period of time and are capable of providing an alarm a few days in advance, helping clinicians in emergency situations, monitor hospitalised patients and identify potentially critical situations at an early stage. However, the pandemic continues to evolve due to new variants, treatments, and vaccines; considering datasets over short periods could not capture this aspect. In addition, these applications often avoid dealing with the uncertainty associated with the prediction provided by machine learning models, potentially causing costly mistakes. In this work, we present a system based on Recurrent Neural Networks (RNN) for the daily estimate of the prognosis of COVID-19 patients that is built and tested using data collected over a long period of time. Our system achieves high predictive performance and uses an algorithm to effectively determine and discard those patients for whom RNN cannot predict the prognosis with sufficient confidence.

Recurrent Neural Networks for Daily Estimation of COVID-19 Prognosis with Uncertainty Handling / Rossetti, Nicholas; Gerevini, Alfonso E.; Olivato, Matteo; Putelli, Luca; Chiari, Mattia; Serina, Ivan; Minisci, Davide; Foca, Emanuele. - 225:(2023), pp. 1542-1551. (Intervento presentato al convegno 27th International Conference on Knowledge-Based and Intelligent Information & Engineering Systems tenutosi a Athens).

Recurrent Neural Networks for Daily Estimation of COVID-19 Prognosis with Uncertainty Handling

Nicholas Rossetti
Primo
;
Alfonso E. Gerevini;
2023

Abstract

Most ML-based applications for COVID-19 assess the general conditions of a patient trained and tested on cohorts of patients collected over a short period of time and are capable of providing an alarm a few days in advance, helping clinicians in emergency situations, monitor hospitalised patients and identify potentially critical situations at an early stage. However, the pandemic continues to evolve due to new variants, treatments, and vaccines; considering datasets over short periods could not capture this aspect. In addition, these applications often avoid dealing with the uncertainty associated with the prediction provided by machine learning models, potentially causing costly mistakes. In this work, we present a system based on Recurrent Neural Networks (RNN) for the daily estimate of the prognosis of COVID-19 patients that is built and tested using data collected over a long period of time. Our system achieves high predictive performance and uses an algorithm to effectively determine and discard those patients for whom RNN cannot predict the prognosis with sufficient confidence.
2023
27th International Conference on Knowledge-Based and Intelligent Information & Engineering Systems
COVID-19; Recurrent Neural Network;Gated Recurrent Unit; Time Distributed; Clinical Data; Uncertainty in ML
04 Pubblicazione in atti di convegno::04b Atto di convegno in volume
Recurrent Neural Networks for Daily Estimation of COVID-19 Prognosis with Uncertainty Handling / Rossetti, Nicholas; Gerevini, Alfonso E.; Olivato, Matteo; Putelli, Luca; Chiari, Mattia; Serina, Ivan; Minisci, Davide; Foca, Emanuele. - 225:(2023), pp. 1542-1551. (Intervento presentato al convegno 27th International Conference on Knowledge-Based and Intelligent Information & Engineering Systems tenutosi a Athens).
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1697118
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