BACKGROUND: The 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 .
OBJECTIVE: The 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 .
METHODS: We 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).
RESULTS: We 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).
CONCLUSIONS: Our 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.