| Téléchargement | - Voir la version finale : A machine learning approach to differentiate between COVID-19 and influenza infection using synthetic infection and immune response data (PDF, 12.2 Mio)
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| DOI | Trouver le DOI : https://doi.org/10.3934/mbe.2022272 |
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| Auteur | Rechercher : Farhang-Sardroodi, SuzanIdentifiant ORCID : https://orcid.org/0000-0001-7654-8804; Rechercher : Ghaemi, Mohammad Sajjad1; Rechercher : Craig, MorganIdentifiant ORCID : https://orcid.org/0000-0003-4852-4770; Rechercher : Ooi, Hsu Kiang1Identifiant ORCID : https://orcid.org/0000-0001-7934-4866; Rechercher : Heffernan, Jane MIdentifiant ORCID : https://orcid.org/0000-0001-9502-1688 |
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| Affiliation | - Conseil national de recherches du Canada. Technologies numériques
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| Format | Texte, Article |
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| Sujet | biological systems; mechanistic model; infectious disease; Influenza (flu); COVID-19; machine learning; classification; logistic regression; regularization; Lasso; Ridge; PLS-DA |
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| Résumé | Data analysis is widely used to generate new insights into human disease mechanisms and provide better treatment methods. In this work, we used the mechanistic models of viral infection to generate synthetic data of influenza and COVID-19 patients. We then developed and validated a supervised machine learning model that can distinguish between the two infections. Influenza and COVID-19 are contagious respiratory illnesses that are caused by different pathogenic viruses but appeared with similar initial presentations. While having the same primary signs COVID-19 can produce more severe symptoms, illnesses, and higher mortality. The predictive model performance was externally evaluated by the ROC AUC metric (area under the receiver operating characteristic curve) on 100 virtual patients from each cohort and was able to achieve at least AUC = 91% using our multiclass classifier. The current investigation highlighted the ability of machine learning models to accurately identify two different diseases based on major components of viral infection and immune response. The model predicted a dominant role for viral load and productively infected cells through the feature selection process. |
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| Date de publication | 2022-04-06 |
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| Maison d’édition | American Institute of Mathematical Sciences (AIMS) |
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| Langue | anglais |
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| Publications évaluées par des pairs | Oui |
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| Exporter la notice | Exporter en format RIS |
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| Signaler une correction | Signaler une correction (s'ouvre dans un nouvel onglet) |
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| Identificateur de l’enregistrement | bd24343e-6a6e-4b56-8a51-e7e9bc3b7eb1 |
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| Enregistrement créé | 2022-04-22 |
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| Enregistrement modifié | 2022-04-22 |
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