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

The copyright holder for this preprint (which was not peer-reviewed) is the . https://doi.org/10.1101/2020.03.20.20037325 doi: medRxiv preprint matrix into an 18-dimensional vector and concatenated it with the 22-dimensional vector to form a 40-dimensional CT feature vector. According to the checkpoints, the CT data sequence with a length of seven and a dimension of 40 was formed. For the sake of combining static and dynamic data as the input of long short term memory (LSTM), a multi-layer perceptron (MLP) was applied to the static data to obtain a 40-dimensional feature vector, which is used as the input data of the first timestamp of the LSTM, followed by the other seven CT feature vectors 18 . The LSTM model employed in this study is a single-layer network with the embedding dimension of 40 and the hidden dimension of 32. The output of the LSTM, a 32 × 8 feature sequence, was then fed into fully connected layers. A Softmax layer was added at the top of the network to output the probability of the patient conversion to the severe/critical stage.