Author: 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
Title: Predicting COVID-19 malignant progression with AI techniques Document date: 2020_3_23
ID: 50oy9qqy_8
Snippet: The pipeline of the prediction model is shown in Figure 3 . The input data includes the static data and the dynamic data, where the static data is a 75-dimensional vector, containing the clinical data and personal information of patients. Dynamic data is a series of quantitative chest CT data collected at different times. Each CT data at different checkpoint consists of a 3 × 6 matrix and a 22-dimensional vector. In order to merge these two part.....
Document: The pipeline of the prediction model is shown in Figure 3 . The input data includes the static data and the dynamic data, where the static data is a 75-dimensional vector, containing the clinical data and personal information of patients. Dynamic data is a series of quantitative chest CT data collected at different times. Each CT data at different checkpoint consists of a 3 × 6 matrix and a 22-dimensional vector. In order to merge these two parts, we directly flattened the All rights reserved. No reuse allowed without permission. author/funder, who has granted medRxiv a license to display the preprint in perpetuity.
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