Predicting COVID-19 malignant progression with AI techniques

Xiang Bai; Cong Fang; Yu Zhou; Song Bai; Zaiyi Liu; Qianlan Chen; Yongchao Xu; Tian Xia; Shi Gong; Xudong Xie; Dejia Song; Ronghui Du; Chunhua Zhou; Chengyang Chen; Dianer Nie; Dandan Tu; Changzheng Zhang; Xiaowu Liu; Lixin Qin; Weiwei Chen

However, most studies focused on cross-sectional description and comparison of clinical, laboratory and CT imaging findings [9] [10] [11] [12] . Some studies focused on seeking risk factors for death outcome 3, 13 . None of them used AI-based methods for progression prediction of mild COVID-19 patients up to date. To solve this problem, we aimed to apply AI techniques to study multivariate heterogeneous data (clinical data and serial chest CT imaging) and to further develop an accurate and effective prediction model. Specifically, we employed a deep learning-based model to effectively mine the complementary information in static clinical data and serial quantitative chest CT sequence. Since deep learning-based methods had been widely adopted and had