A Clinical Decision Web to Predict ICU Admission or Death for Patients Hospitalised with COVID-19 Using Machine Learning Algorithms
| Autor: | David Abadía-Gallego, Angel Lanas, Gorka Labata-Lezaun, Luis M. Esteban, Rafael del-Hoyo-Alonso, M. José Esquillor-Rodrigo, J. Ramón Paño-Pardo, M. Trinidad Serrano, Rocío Aznar-Gimeno |
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| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Calibration (statistics)
Computer science Health Toxicology and Mutagenesis Vital signs Machine learning computer.software_genre predictive model Machine Learning medicine Humans clinical decision web tool Retrospective Studies business.industry SARS-CoV-2 Public Health Environmental and Occupational Health COVID-19 Retrospective cohort study medicine.disease Comorbidity mortality Random forest Intensive Care Units Multilayer perceptron Cohort Perspective ICU Medicine Metric (unit) Artificial intelligence business Algorithm computer Algorithms |
| Zdroj: | International Journal of Environmental Research and Public Health Zaguán. Repositorio Digital de la Universidad de Zaragoza instname International Journal of Environmental Research and Public Health, Vol 18, Iss 8677, p 8677 (2021) |
| ISSN: | 1660-4601 1661-7827 |
| Popis: | The purpose of the study was to build a predictive model for estimating the risk of ICU admission or mortality among patients hospitalized with COVID-19 and provide a user-friendly tool to assist clinicians in the decision-making process. The study cohort comprised 3623 patients with confirmed COVID-19 who were hospitalized in the SALUD hospital network of Aragon (Spain), which includes 23 hospitals, between February 2020 and January 2021, a period that includes several pandemic waves. Up to 165 variables were analysed, including demographics, comorbidity, chronic drugs, vital signs, and laboratory data. To build the predictive models, different techniques and machine learning (ML) algorithms were explored: multilayer perceptron, random forest, and extreme gradient boosting (XGBoost). A reduction dimensionality procedure was used to minimize the features to 20, ensuring feasible use of the tool in practice. Our model was validated both internally and externally. We also assessed its calibration and provide an analysis of the optimal cut-off points depending on the metric to be optimized. The best performing algorithm was XGBoost. The final model achieved good discrimination for the external validation set (AUC = 0.821, 95% CI 0.787–0.854) and accurate calibration (slope = 1, intercept = −0.12). A cut-off of 0.4 provides a sensitivity and specificity of 0.71 and 0.78, respectively. In conclusion, we built a risk prediction model from a large amount of data from several pandemic waves, which had good calibration and discrimination ability. We also created a user-friendly web application that can aid rapid decision-making in clinical practice. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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