Machine learning predicts pulmonary Long Covid sequelae using clinical data

Long COVID is a multi-systemic disease characterized by the persistence or occurrence of many symptoms that in many cases affect the pulmonary system. These, in turn, may deteriorate the patient's quality of life making it easier to develop severe complications. Being able to predict this syndrome is therefore important as this enables early treatment. In this work, we investigated three machine learning approaches that use clinical data collected at the time of hospitalization to this goal. The first works with all the descriptors feeding a traditional shallow learner, the second exploits the benefits of an ensemble of classifiers, and the third is driven by the intrinsic multimodality of the data so that different models learn complementary information. The experiments on a new cohort of data from 152 patients show that it is possible to predict pulmonary Long Covid sequelae with an accuracy of up to <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mn>94</mml:mn> <mml:mo>%</mml:mo></mml:mrow> </mml:math> . As a further contribution, this work also publicly discloses the related data repository to foster research in this field.

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Publication Details

Journal
BMC Medical Informatics and Decision Making
Published
2024-11-27
DOI
https://doi.org/10.1186/s12911-024-02745-3
Citations
5
Primary Topic
Long-Term Effects of COVID-19
Type
article
Field-Weighted Citation Impact
1.85
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article

Machine learning predicts pulmonary Long Covid sequelae using clinical data

Paolo Soda, Christian Salvatore, Diego Sona, Lorenzo Preda et al.
5 citations
BMC Medical Informatics and Decision Making
Long-Term Effects of COVID-19
1.85
article

Machine learning predicts pulmonary Long Covid sequelae using clinical data

Paolo Soda, Christian Salvatore, Diego Sona, Lorenzo Preda, Elia Schiavon, Luisa Francini, Deborah Fazzini, Ermanno Cordelli, Isabella Castiglioni, Michaela Cellina, Andrea Cozzi, Sergio Papa, Marco Alì, Matteo Tortora, Chandra Bortolotto, Sara Citter, Greta Clementi
article en
5 citations

Abstract

Long COVID is a multi-systemic disease characterized by the persistence or occurrence of many symptoms that in many cases affect the pulmonary system. These, in turn, may deteriorate the patient's quality of life making it easier to develop severe complications. Being able to predict this syndrome is therefore important as this enables early treatment. In this work, we investigated three machine learning approaches that use clinical data collected at the time of hospitalization to this goal. The first works with all the descriptors feeding a traditional shallow learner, the second exploits the benefits of an ensemble of classifiers, and the third is driven by the intrinsic multimodality of the data so that different models learn complementary information. The experiments on a new cohort of data from 152 patients show that it is possible to predict pulmonary Long Covid sequelae with an accuracy of up to <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mn>94</mml:mn> <mml:mo>%</mml:mo></mml:mrow> </mml:math> . As a further contribution, this work also publicly discloses the related data repository to foster research in this field.

BMC Medical Informatics and Decision MakingVol. 24(1)
Università Campus Bio-Medico (IT), University of Trento (IT), Fondazione Bruno Kessler (IT), University of Pavia (IT), Istituto Universitario di Studi Superiori di Pavia (IT), Ente Ospedaliero Cantonale (CH), Bracco (Italy) (IT), Centro Diagnostico Italiano (IT), Policlinico San Matteo Fondazione (IT), Istituti di Ricovero e Cura a Carattere Scientifico (IT), ASST Fatebenefratelli Sacco (IT), Istituto Imaging della Svizzera Italiana, University of Milano-Bicocca (IT), University of Genoa (IT), Umeå University (SE)
Openalex Percentile: Top 13%
Long-Term Effects of COVID-19
1.85
DOIOpen Access PDFOpenAlex
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