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Medical Journal of Islamic Republic of Iran، جلد ۳۶، شماره ۱، صفحات ۲۲۷-۲۳۵
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عنوان انگلیسی Predicting the Need for Intubation among COVID-19 Patients Using Machine Learning Algorithms: A Single-Center Study
چکیده انگلیسی مقاله 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.
کلیدواژه‌های انگلیسی مقاله COVID-19, Coronavirus, Machine Learning, Intubation, Prognosis, Mechanical Ventilator
نویسندگان مقاله | Raoof Nopour
Student Research Committee, School of Health Management and Information Sciences Branch, Iran University of Medical Sciences, Tehran, Iran


| Mostafa Shanbehzadeh
2. Department of Health Information Technology, School of Paramedical, Ilam University of Medical Sciences, Ilam, Iran


| Hadi Kazemi-Arpanahi
Department of Health Information Technology, & Department of Student Research Committee, Abadan University of Medical Sciences, Abadan, Iran
نشانی اینترنتی http://mjiri.iums.ac.ir/browse.php?a_code=A-10-6143-6&slc_lang=en&sid=1
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کد مقاله (doi)
زبان مقاله منتشر شده en
موضوعات مقاله منتشر شده COVID 19
نوع مقاله منتشر شده Original Research
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