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In JMIR medical informatics ; h5-index 23.0

BACKGROUND : Most of mortality of COVID-19 were from severe patients.

OBJECTIVE : Effective treatment of these severe cases remains a challenge due to a lack of early detection.

METHODS : A total set of 27 severe and 151 non-severe clinical and computerized tomography (CT) records from 46 COVID-19 patients (10 severe, 36 non-severe) was collected for building the model. Using a recently published convolutional neural network (CNN), we managed to extract features from CT images. A machine learning model which combines these features with clinical laboratory results was also 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 developed a statistical model for forecasting severity based on patients' laboratory tests results before turning into severe cases, with an AUROC score of 0.81.

CONCLUSIONS : To our knowledge, this is the first report on predicting COVID-19 patient's severity progression, as well as severity forecasting, through a combination analysis of laboratory tests and CT images.

CLINICALTRIAL :

Zhu Fang, Li Daowei, Zhang Qiang, Tan Yue, Yue Yuanyi, Bai Yuhan, Li Jimeng, Li Jiahang, Feng Xinghuo, Chen Shiyu, Xu Youjun, Xiao Si-Yu, Sun Muyan, Li Xiaona

2020-Sep-21