Volume 7, Issue 3 (September 2022)
J Environ Health Sustain Dev 2022, 7(3): 1698-1707 |
Back to browse issues page
Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
Afrash M R, Kazemi-Arpanahi H, Nopour R, Tabatabaei E S, Shanbehzadeh M. Proposing an Intelligent Monitoring System for Early Prediction of Need for Intubation among COVID-19 Hospitalized Patients. J Environ Health Sustain Dev 2022; 7 (3) :1698-1707
URL: http://jehsd.ssu.ac.ir/article-1-451-en.html
URL: http://jehsd.ssu.ac.ir/article-1-451-en.html
Mohammad Reza Afrash 
, Hadi Kazemi-Arpanahi , Raoof Nopour , Elmira Sadat Tabatabaei , Mostafa Shanbehzadeh *
, Hadi Kazemi-Arpanahi , Raoof Nopour , Elmira Sadat Tabatabaei , Mostafa Shanbehzadeh *
Department of Health Information Technology, School of Paramedical, Ilam University of Medical Sciences, Ilam, Iran.
Abstract: (958 Views)
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 ML-based intelligent models, notably the XGBoost algorithm, actually enables recognizing high-risk cases and advising correct therapeutic and supportive care by the clinicians.
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 ML-based intelligent models, notably the XGBoost algorithm, actually enables recognizing high-risk cases and advising correct therapeutic and supportive care by the clinicians.
Type of Study: Original articles |
Subject:
Biostatistics
Received: 2022/05/25 | Accepted: 2022/07/10 | Published: 2022/09/30
Received: 2022/05/25 | Accepted: 2022/07/10 | Published: 2022/09/30
Send email to the article author
| Rights and permissions | |
 |
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. |
