Publication: A predictive model for hospitalization and survival to COVID‑19 in a retrospective population‑based study
Authors
Cisterna García, Alejandro ; Guillén Teruel, Antonio ; Caracena, Marcos ; Pérez-Cuadrado Martínez, Enrique ; Jiménez Barrionuevo, Fernando ; Francisco Verdú, Francisco J. ; Reina, Gabriel ; González Billalabeitia, Enrique ; Palma Méndez, José Tomás ; Sánchez Ferrer, Álvaro ; Botía Blaya, Juan Antonio
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Facultades de la UMU::Facultad de Informática
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Publisher
Nature Research
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DOI
https://doi.org/10.1038/s41598-022-22547-9
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info:eu-repo/semantics/article
Description
Abstract
The development of tools that provide early triage of COVID-19 patients with minimal use of diagnostic tests, based on easily accessible data, can be of vital importance in reducing COVID-19 mortality rates during high-incidence scenarios. This work proposes a machine learning model to predict mortality and risk of hospitalization using both 2 simple demographic features and 19 comorbidities obtained from 86,867 electronic medical records of COVID-19 patients, and a new method (LR-IPIP) designed to deal with data imbalance problems. The model was able to predict with high accuracy (90–93%, ROC-AUC = 0.94) the patient's final status (deceased or discharged), while its accuracy was medium (71–73%, ROC-AUC = 0.75) with respect to the risk of hospitalization. The most relevant characteristics for these models were age, sex, number of comorbidities, osteoarthritis, obesity, depression, and renal failure. Finally, to facilitate its use by clinicians, a user-friendly website has been developed (https://alejandrocisterna.shinyapps.io/PROVIA).
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Citation
Scientific Reports, 2022, Vol. 12 : 18126
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Este ítem está sujeto a una licencia Creative Commons. http://creativecommons.org/licenses/by/4.0/