Nopour Raoof, Shanbehzadeh Mostafa, Kazemi-Arpanahi Hadi
Student Research Committee, School of Health Management and Information Sciences Branch, Iran University of Medical Sciences, Tehran, Iran.
Department of Health Information Technology, School of Paramedical, Ilam University of Medical Sciences, Ilam, Iran.
Med J Islam Repub Iran. 2022 Apr 4;36:30. doi: 10.47176/mjiri.36.30. eCollection 2022.
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. 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. 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. 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.
由于呼吸机短缺，对于2019冠状病毒病（COVID-19）重症患者，迫切需要一种客观准确的预后评估，这可能需要使用有创机械通气（MV）。本研究旨在使用机器学习（ML）算法构建一个预测模型，以便一线临床医生更好地对濒危患者进行分诊，并确定需要有创机械通气的优先顺序。在这项回顾性单中心研究中，对2020年2月9日至2020年12月20日期间482例COVID-19患者的数据，采用包括多层感知器（MLP）、逻辑回归（LR）、J-48决策树和朴素贝叶斯（NB）在内的多种机器学习算法进行了分析。首先，使用P<0.01的卡方检验确定最重要的临床变量。然后，通过使用包括真阳性率、假阳性率、精确率、召回率、F值、马修斯相关系数（MCC）和卡帕值等评估标准比较机器学习算法的性能，确定表现最佳的算法。使用15个经过验证的特征训练预测模型，这些特征包括咳嗽、挫伤、氧疗、呼吸困难、味觉丧失、流涕、血压、绝对淋巴细胞计数、胸腔积液、活化部分凝血活酶时间、血糖、白细胞计数、心脏病、住院时间以及其他基础疾病。结果表明，F值=0.868、曲线下面积（AUC）=0.892的J-48决策树在预测插管需求方面表现最佳。机器学习算法有潜力改进传统临床标准，以预测COVID-19住院患者的插管必要性。这种基于机器学习的预测模型可能有助于医生优化插管时机，更好地分配有创机械通气资源和人员，并改善患者的临床状况。
