Academic Journal
Machine learning in predicting respiratory failure in patients with COVID-19 pneumonia-Challenges, strengths, and opportunities in a global health emergency.
| Title: | Machine learning in predicting respiratory failure in patients with COVID-19 pneumonia-Challenges, strengths, and opportunities in a global health emergency. |
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| Authors: | Davide Ferrari, Jovana Milic, Roberto Tonelli, Francesco Ghinelli, Marianna Meschiari, Sara Volpi, Matteo Faltoni, Giacomo Franceschi, Vittorio Iadisernia, Dina Yaacoub, Giacomo Ciusa, Erica Bacca, Carlotta Rogati, Marco Tutone, Giulia Burastero, Alessandro Raimondi, Marianna Menozzi, Erica Franceschini, Gianluca Cuomo, Luca Corradi, Gabriella Orlando, Antonella Santoro, Margherita Digaetano, Cinzia Puzzolante, Federica Carli, Vanni Borghi, Andrea Bedini, Riccardo Fantini, Luca Tabbì, Ivana Castaniere, Stefano Busani, Enrico Clini, Massimo Girardis, Mario Sarti, Andrea Cossarizza, Cristina Mussini, Federica Mandreoli, Paolo Missier, Giovanni Guaraldi |
| Source: | PLoS ONE, Vol 15, Iss 11, p e0239172 (2020) |
| Publisher Information: | Public Library of Science (PLoS), 2020. |
| Publication Year: | 2020 |
| Collection: | LCC:Medicine LCC:Science |
| Subject Terms: | Medicine, Science |
| More Details: | AimsThe aim of this study was to estimate a 48 hour prediction of moderate to severe respiratory failure, requiring mechanical ventilation, in hospitalized patients with COVID-19 pneumonia.MethodsThis was an observational prospective study that comprised consecutive patients with COVID-19 pneumonia admitted to hospital from 21 February to 6 April 2020. The patients' medical history, demographic, epidemiologic and clinical data were collected in an electronic patient chart. The dataset was used to train predictive models using an established machine learning framework leveraging a hybrid approach where clinical expertise is applied alongside a data-driven analysis. The study outcome was the onset of moderate to severe respiratory failure defined as PaO2/FiO2 ratio ResultsA total of 198 patients contributed to generate 1068 usable observations which allowed to build 3 predictive models based respectively on 31-variables signs and symptoms, 39-variables laboratory biomarkers and 91-variables as a composition of the two. A fourth "boosted mixed model" included 20 variables was selected from the model 3, achieved the best predictive performance (AUC = 0.84) without worsening the FN rate. Its clinical performance was applied in a narrative case report as an example.ConclusionThis study developed a machine model with 84% prediction accuracy, which is able to assist clinicians in decision making process and contribute to develop new analytics to improve care at high technology readiness levels. |
| Document Type: | article |
| File Description: | electronic resource |
| Language: | English |
| ISSN: | 1932-6203 |
| Relation: | https://doaj.org/toc/1932-6203 |
| DOI: | 10.1371/journal.pone.0239172 |
| Access URL: | https://doaj.org/article/9bc9a8f37b0943fa92c27326d11d56a0 |
| Accession Number: | edsdoj.9bc9a8f37b0943fa92c27326d11d56a0 |
| Database: | Directory of Open Access Journals |
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| ISSN: | 19326203 |
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| DOI: | 10.1371/journal.pone.0239172 |
| Published in: | PLoS ONE |
| Language: | English |