Proposing an Intelligent Monitoring System for Early Prediction of Need for Intubation among COVID-19 Hospitalized Patients
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 eff...
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| Format: | Article |
| Language: | English |
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Shahid Sadoughi University of Medical Sciences
2022-09-01
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| Series: | Journal of Environmental Health and Sustainable Development |
| Subjects: | |
| Online Access: | http://jehsd.ssu.ac.ir/article-1-451-en.html |
| _version_ | 1828195610559250432 |
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| author | Mohammad Reza Afrash Hadi Kazemi-Arpanahi Raoof Nopour Elmira Sadat Tabatabaei Mostafa Shanbehzadeh |
| author_facet | Mohammad Reza Afrash Hadi Kazemi-Arpanahi Raoof Nopour Elmira Sadat Tabatabaei Mostafa Shanbehzadeh |
| author_sort | Mohammad Reza Afrash |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-12T09:46:40Z |
| format | Article |
| id | doaj.art-6e6e582d7fbb49c2aa444dfdedc51614 |
| institution | Directory Open Access Journal |
| issn | 2476-6267 2476-7433 |
| language | English |
| last_indexed | 2024-04-12T09:46:40Z |
| publishDate | 2022-09-01 |
| publisher | Shahid Sadoughi University of Medical Sciences |
| record_format | Article |
| series | Journal of Environmental Health and Sustainable Development |
| spelling | doaj.art-6e6e582d7fbb49c2aa444dfdedc516142022-12-22T03:37:56ZengShahid Sadoughi University of Medical SciencesJournal of Environmental Health and Sustainable Development2476-62672476-74332022-09-017316981707Proposing an Intelligent Monitoring System for Early Prediction of Need for Intubation among COVID-19 Hospitalized PatientsMohammad Reza Afrash0Hadi Kazemi-Arpanahi1Raoof Nopour2Elmira Sadat Tabatabaei3Mostafa Shanbehzadeh4 Department of Medical Informatics, Student Research Committee, School of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran. Department of Health Information Technology, Abadan University of Medical Sciences, Abadan, Iran. Department of Health Information Management, Student Research Committee, School of Health Management and Information Sciences Branch, Iran University of Medical Sciences, Tehran, Iran. Department of Genetics, Islamic Azad University, Tehran Medical Branch, Tehran, Iran. Department of Health Information Technology, School of Paramedical, Ilam University of Medical Sciences, Ilam, Iran. 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.http://jehsd.ssu.ac.ir/article-1-451-en.htmlcovid-19coronavirusartificial intelligencemachine learningintubationprognosis. |
| spellingShingle | Mohammad Reza Afrash Hadi Kazemi-Arpanahi Raoof Nopour Elmira Sadat Tabatabaei Mostafa Shanbehzadeh Proposing an Intelligent Monitoring System for Early Prediction of Need for Intubation among COVID-19 Hospitalized Patients Journal of Environmental Health and Sustainable Development covid-19 coronavirus artificial intelligence machine learning intubation prognosis. |
| title | Proposing an Intelligent Monitoring System for Early Prediction of Need for Intubation among COVID-19 Hospitalized Patients |
| title_full | Proposing an Intelligent Monitoring System for Early Prediction of Need for Intubation among COVID-19 Hospitalized Patients |
| title_fullStr | Proposing an Intelligent Monitoring System for Early Prediction of Need for Intubation among COVID-19 Hospitalized Patients |
| title_full_unstemmed | Proposing an Intelligent Monitoring System for Early Prediction of Need for Intubation among COVID-19 Hospitalized Patients |
| title_short | Proposing an Intelligent Monitoring System for Early Prediction of Need for Intubation among COVID-19 Hospitalized Patients |
| title_sort | proposing an intelligent monitoring system for early prediction of need for intubation among covid 19 hospitalized patients |
| topic | covid-19 coronavirus artificial intelligence machine learning intubation prognosis. |
| url | http://jehsd.ssu.ac.ir/article-1-451-en.html |
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