Repository of Research and Investigative Information

Repository of Research and Investigative Information

Ilam University of Medical Sciences

Predicting the Need for Intubation among COVID-19 Patients Using Machine Learning Algorithms: A Single-Center Study

Fri Oct 7 02:33:01 2022

(2022) Predicting the Need for Intubation among COVID-19 Patients Using Machine Learning Algorithms: A Single-Center Study. Medical Journal of the Islamic Republic of Iran. ISSN 10161430 (ISSN)

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

Abstract

Background: Owing to the shortage of ventilators, there is a crucial demand for an objective and accurate prognosis for 2019 coronavirus disease (COVID-19) critical patients, which may necessitate a mechanical ventilator (MV). This study aimed to construct a predictive model using machine learning (ML) algorithms for frontline clinicians to better triage endangered patients and priorities who would need MV. Methods: In this retrospective single-center study, the data of 482 COVID-19 patients from February 9, 2020, to December 20, 2020, were analyzed by several ML algorithms including, multi-layer perception (MLP), logistic regression (LR), J-48 decision tree, and Naïve Bayes (NB). First, the most important clinical variables were identified using the Chi-square test at P < 0.01. Then, by comparing the ML algorithms' performance using some evaluation criteria, including TP-Rate, FP-Rate, precision, recall, F-Score, MCC, and Kappa, the best performing one was identified. Results: Predictive models were trained using 15 validated features, including cough, contusion, oxygen therapy, dyspnea, loss of taste, rhinorrhea, blood pressure, absolute lymphocyte count, pleural fluid, activated partial thromboplastin time, blood glucose, white cell count, cardiac diseases, length of hospitalization, and other underline diseases. The results indicated the J-48 with F-score = 0.868 and AUC = 0.892 yielded the best performance for predicting intubation requirement. Conclusion: ML algorithms are potentials to improve traditional clinical criteria to forecast the necessity for intubation in COVID-19 in-hospital patients. Such ML-based prediction models may help physicians with optimizing the timing of intubation, better sharing of MV resources and personnel, and increase patient clinical status. © 2022 Iran University of Medical Sciences. All Rights Reserved.

Item Type: Article
Creators:
CreatorsEmail
Nopour, R.UNSPECIFIED
Shanbehzadeh, M.UNSPECIFIED
Kazemi-Arpanahi, H.UNSPECIFIED
Keywords: Coronavirus COVID-19 Intubation Machine Learning Mechanical Ventilator Prognosis
Divisions:
Journal or Publication Title: Medical Journal of the Islamic Republic of Iran
Journal Index: Scopus
Volume: 36
Number: 1
Identification Number: https://doi.org/10.47176/mjiri.36.30
ISSN: 10161430 (ISSN)
Depositing User: مهندس مهدی شریفی
URI: http://eprints.medilam.ac.ir/id/eprint/3879

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