One of the main problems in poorly controlled asthma is the access to the Emergency Department (ED). Using a machine learning (ML) approach, the aim of our study was to identify the main predictors of severe asthma exacerbations requiring hospital admission.

A machine learning approach to characterize patients with asthma exacerbation attending an acute care setting / D'Amato, Maria; Ambrosino, Pasquale; Simioli, Francesca; Adamo, Sarah; Stanziola, Anna Agnese; D'Addio, Giovanni; Molino, Antonio; Maniscalco, Mauro. - In: EUROPEAN JOURNAL OF INTERNAL MEDICINE. - ISSN 0953-6205. - (2022). [10.1016/j.ejim.2022.07.019]

A machine learning approach to characterize patients with asthma exacerbation attending an acute care setting

Ambrosino, Pasquale;Simioli, Francesca;Stanziola, Anna Agnese;D'Addio, Giovanni;Molino, Antonio;Maniscalco, Mauro
2022

Abstract

One of the main problems in poorly controlled asthma is the access to the Emergency Department (ED). Using a machine learning (ML) approach, the aim of our study was to identify the main predictors of severe asthma exacerbations requiring hospital admission.
2022
A machine learning approach to characterize patients with asthma exacerbation attending an acute care setting / D'Amato, Maria; Ambrosino, Pasquale; Simioli, Francesca; Adamo, Sarah; Stanziola, Anna Agnese; D'Addio, Giovanni; Molino, Antonio; Maniscalco, Mauro. - In: EUROPEAN JOURNAL OF INTERNAL MEDICINE. - ISSN 0953-6205. - (2022). [10.1016/j.ejim.2022.07.019]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11588/891643
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