During 2020 and 2021, managing limited healthcare resources and hospital beds has been a fundamental aspect of the fight against the COVID-19 pandemic. Predicting in advance the length of stay, and in particular identifying whether a patient is going to stay in the hospital longer or less than a week, can provide important support in handling resources allocation. However, there have been significant changes in terms of containment measures, virus diffusion, new treatments, vaccines, and new variants of SARS-CoV-2 during the last period. These changes pose several conceptual drift issues that can limit the usefulness of machine learning in this context. In this work, we present a machine learning system trained and tested using data from more than 6000 hospitalised patients in northern Italy, distributed over almost two years of pandemic. We show how machine learning can be effective even by analysing data over this long period of time, also exploiting a model that predicts the patient's outcome in terms of discharge or death. Furthermore, learning from data that also consider deceased patients is a common issue in predicting the length of stay because they have severe conditions similar to patients with a long stay period, but may actually have a very short duration of hospitalisation. For this purpose, we present a method for handling data from alive and deceased patients, exploiting more patient records, increasing the robustness of the model and its performance in this task. Finally, we investigate the features that are most relevant to the prediction of the simplified length of stay.

Machine Learning Models for Predicting Short-Long Length of Stay of COVID-19 Patients / Olivato, M.; Rossetti, N.; Gerevini, A. E.; Chiari, M.; Putelli, L.; Serina, I.. - 207:(2022), pp. 1232-1241. (Intervento presentato al convegno 26th International Conference on Knowledge-Based and Intelligent Information and Engineering Systems, KES 2022 tenutosi a ita) [10.1016/j.procs.2022.09.179].

Machine Learning Models for Predicting Short-Long Length of Stay of COVID-19 Patients

Rossetti N.;Gerevini A. E.;
2022

Abstract

During 2020 and 2021, managing limited healthcare resources and hospital beds has been a fundamental aspect of the fight against the COVID-19 pandemic. Predicting in advance the length of stay, and in particular identifying whether a patient is going to stay in the hospital longer or less than a week, can provide important support in handling resources allocation. However, there have been significant changes in terms of containment measures, virus diffusion, new treatments, vaccines, and new variants of SARS-CoV-2 during the last period. These changes pose several conceptual drift issues that can limit the usefulness of machine learning in this context. In this work, we present a machine learning system trained and tested using data from more than 6000 hospitalised patients in northern Italy, distributed over almost two years of pandemic. We show how machine learning can be effective even by analysing data over this long period of time, also exploiting a model that predicts the patient's outcome in terms of discharge or death. Furthermore, learning from data that also consider deceased patients is a common issue in predicting the length of stay because they have severe conditions similar to patients with a long stay period, but may actually have a very short duration of hospitalisation. For this purpose, we present a method for handling data from alive and deceased patients, exploiting more patient records, increasing the robustness of the model and its performance in this task. Finally, we investigate the features that are most relevant to the prediction of the simplified length of stay.
2022
26th International Conference on Knowledge-Based and Intelligent Information and Engineering Systems, KES 2022
machine learning; COVID19; lenght of stay
04 Pubblicazione in atti di convegno::04b Atto di convegno in volume
Machine Learning Models for Predicting Short-Long Length of Stay of COVID-19 Patients / Olivato, M.; Rossetti, N.; Gerevini, A. E.; Chiari, M.; Putelli, L.; Serina, I.. - 207:(2022), pp. 1232-1241. (Intervento presentato al convegno 26th International Conference on Knowledge-Based and Intelligent Information and Engineering Systems, KES 2022 tenutosi a ita) [10.1016/j.procs.2022.09.179].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1671189
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