Abstract
BackgroundThe number of deaths from COVID-19 continues to surge worldwide. In particular, if a patient’s condition is sufficiently severe to require invasive ventilation, it is more likely to lead to death than to recovery.ObjectiveThe goal of our study was to analyze the factors related to COVID-19 severity in patients and to develop an artificial intelligence (AI) model to predict the severity of COVID-19 at an early stage.MethodsWe developed an AI model that predicts severity based on data from 5601 COVID-19 patients from all national and regional hospitals across South Korea as of April 2020. The clinical severity of COVID-19 was divided into two categories: low and high severity. The condition of patients in the low-severity group corresponded to no limit of activity, oxygen support with nasal prong or facial mask, and noninvasive ventilation. The condition of patients in the high-severity group corresponded to invasive ventilation, multi-organ failure with extracorporeal membrane oxygenation required, and death. For the AI model input, we used 37 variables from the medical records, including basic patient information, a physical index, initial examination findings, clinical findings, comorbid diseases, and general blood test results at an early stage. Feature importance analysis was performed with AdaBoost, random forest, and eXtreme Gradient Boosting (XGBoost); the AI model for predicting COVID-19 severity among patients was developed with a 5-layer deep neural network (DNN) with the 20 most important features, which were selected based on ranked feature importance analysis of 37 features from the comprehensive data set. The selection procedure was performed using sensitivity, specificity, accuracy, balanced accuracy, and area under the curve (AUC).ResultsWe found that age was the most important factor for predicting disease severity, followed by lymphocyte level, platelet count, and shortness of breath or dyspnea. Our proposed 5-layer DNN with the 20 most important features provided high sensitivity (90.2%), specificity (90.4%), accuracy (90.4%), balanced accuracy (90.3%), and AUC (0.96).ConclusionsOur proposed AI model with the selected features was able to predict the severity of COVID-19 accurately. We also made a web application so that anyone can access the model. We believe that sharing the AI model with the public will be helpful in validating and improving its performance.
Highlights
The COVID-19 pandemic has had a major impact on health care systems globally
Our proposed artificial intelligence (AI) model with the selected features was able to predict the severity of COVID-19 accurately
We believe that sharing the AI model with the public will be helpful in validating and improving its performance
Summary
The COVID-19 pandemic has had a major impact on health care systems globally. Several models have been proposed to predict the severity or mortality of COVID-19 patients using artificial intelligence (AI) techniques. The majority of them have been developed based on limited information or variables, such as medical images [2,3,4,5,6,7], blood and/or urine information [8,9], clinical characteristics [10,11,12], individual-level epidemiological data sets [13], and electronic health records (ie, demographics, laboratory results, medical history, and vital signs) during hospitalization [14]. If a patient’s condition is sufficiently severe to require invasive ventilation, it is more likely to lead to death than to recovery
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have