Repository of Research and Investigative Information

Repository of Research and Investigative Information

Ilam University of Medical Sciences

Proposing an Intelligent Monitoring System for Early Prediction of Need for Intubation among COVID-19 Hospitalized Patients

Sun Apr 21 16:11:18 2024

(2022) Proposing an Intelligent Monitoring System for Early Prediction of Need for Intubation among COVID-19 Hospitalized Patients. Journal of Environmental Health and Sustainable Development. pp. 1698-1707. ISSN 24766267 (ISSN)

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Official URL: https://www.scopus.com/inward/record.uri?eid=2-s2....

Abstract

Introduction: Predicting acute respiratory insufficiency due to coronavirus disease 2019 (COVID-19) can diminish the severe complications and mortality associated with the disease. This study aimed to develop an intelligent system based on machine learning (ML) models for frontline clinicians to effectively triage high-risk patients and prioritize who needs mechanical intubation (MI). Materials and Methods: In this retrospective-design study, the data regarding 482 COVID-19 hospitalized patients from February 9, 2020, to July 20, 2021, was analyzed by six ML classifiers. The most critical clinical variables were identified by a minimal-redundancy-maximal-relevance (mRMR) feature selection technique. In the next step, the models' performance was assessed using confusion matrix criteria and, finally, the best model was adopted. Results: Proposed models were implemented using 23 confirmed variables. Results of comparing six selected ML algorithms indicated the extreme gradient boosting (XGBoost) classifier with 84.7 accuracy, 76.5 specificity, 90.7 sensitivity, 85.1 f-measure, 87.4 Kappa statistic, and 85.3 for receiver operating characteristic (ROC) had the best performance in the intubation prediction. Conclusion: It is found that ML enables a satisfactory accuracy level in calculating intubation risk in COVID-19 patients. Therefore, using the MLbased intelligent models, notably the XGBoost algorithm, actually enables recognizing high-risk cases and advising correct therapeutic and supportive care by the clinicians. © 2022, Journal of Environmental Health and Sustainable Development. All Rights Reserved.

Item Type: Article
Creators:
CreatorsEmail
Afrash, M. R.UNSPECIFIED
Kazemi-Arpanahi, H.UNSPECIFIED
Nopour, R.UNSPECIFIED
Tabatabaei, E. S.UNSPECIFIED
Shanbehzadeh, M.UNSPECIFIED
Keywords: Artificial intelligence Coronavirus Covid-19 Intubation Machine learning Prognosis
Divisions:
Page Range: pp. 1698-1707
Journal or Publication Title: Journal of Environmental Health and Sustainable Development
Journal Index: Scopus
Volume: 7
Number: 3
Identification Number: https://doi.org/10.18502/jehsd.v7i3.10719
ISSN: 24766267 (ISSN)
Depositing User: مهندس مهدی شریفی
URI: http://eprints.medilam.ac.ir/id/eprint/4303

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