JMIR Publications

ABSTRACT

Background:

Most of mortality of COVID-19 were from severe patients. Effective treatment of these severe cases remains a challenge due to a lack of early detection.

Objective:

Herein, we presented a prediction model, combining the radiological outcome with the clinical biochemical indexes, to identify the severe cases.

Methods:

A total set of 27 severe and 151 non-severe clinical and CT (computerized tomography) records from 46 COVID-19 patients (10 severe, 36 non-severe) was collected for building the model. Using a recent published CNN (convolutional neural network), we managed to extract features from CT images. A machine learning model which combines these features with clinical laboratory results also was trained.

Results:

Herein, we presented a prediction model, combining the radiological outcome with the clinical biochemical indexes, to identify the severe cases. The prediction model yields a cross-validated AUROC score of 0.93 and F1 score of 0.89, which improved 6% and 15%, respectively, from the models with laboratory tests features only. In addition, we also developed a statistical model for forecasting severity based on patients’ laboratory tests results before turning severe cases, with an AUROC score of 0.81.

Conclusions:

To our knowledge, this is the first report to predict COVID-19 patient’s severity progression, as well as severity forecasting, through a combination analysis of laboratory tests and CT images.