Predictive Modeling of Hospital Length of Stay in COVID-۱۹ Patients Using Machine Learning Algorithms
- سال انتشار: 1400
- محل انتشار: مجله علوم دارویی و شیمی، دوره: 4، شماره: 5
- کد COI اختصاصی: JR_JMCH-4-5_015
- زبان مقاله: انگلیسی
- تعداد مشاهده: 249
نویسندگان
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 Scientometrics, Ilam University of Medical Sciences, Ilam, Iran
Department of Health Information Technology and Management, School of Paramedical, Tehran University of Medical Sciences, Tehran, Iran
Department of Operating Room, Faculty of Allied Medical Sciences, Lorestan University of Medical Sciences, Khorramabad, Iran
Department of Health Information Technology, School of Allied Medical Sciences, Lorestan University of Medical Sciences, Khorramabad, Iran
Department of Health Information Technology, School of Paramedical, Ilam University of Medical Sciences, Ilam, Iran
چکیده
The rapid worldwide outbreak of COVID-۱۹ has posed serious and unprecedented challenges to healthcare systems in predicting disease behavior, consequences and resource utilization. Therefore, predicting the Length of Stay (LOS) is necessary to ensure optimal allocate of scarce hospital resources. The purpose of this research was to construct a model for predicting COVID-۱۹ patients' hospital LOS by multiple Machine Learning (ML) algorithms. Using a single-center registry, we studied the records of ۱۲۲۵ laboratory-confirmed COVID-۱۹ hospitalized patients from February ۹, ۲۰۲۰, to December ۲۰, ۲۰۲۰. The most important clinical parameters in the COVID-۱۹ LOS prediction were identified with a correlation coefficient at the P-value< ۰.۲. Then, the prediction models were developed based on seven ML techniques according to selected variables. Finally, to evaluate the performances of those models several standard quantitative measures includes accuracy, sensitivity, specificity and ROC curve were used to evaluate the proposed predictive models. After implementing feature selection, a total of ۲۰ variables was identified as the most relevant predictors to build the prediction models. The results indicated that the best performance belonged to the Support Vector Machine (SVM) algorithm with the mean accuracy of ۹۹.۵%, mean specificity of ۹۹.۷%, mean sensitivity of ۹۹.۴%, and the standard deviation of ۱.۲. The SVM provided a reasonable level of accuracy and certainty in predicting the LOS in COVID-۱۹ patients and potentially facilitates hospital bed management, turnover and optimized resource allocation.کلیدواژه ها
COVID-۱۹, Coronavirus, Machine Learning, Data mining, Length of stayاطلاعات بیشتر در مورد COI
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