Selected article for: "multi stage method and traditional multi stage method"
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_32
    Snippet: Compared with traditional multi-stage methods, we demonstrate that our deep learning-based method can extract spatial and temporal information efficiently and improve the prediction performance significantly. The ability to identify patients with potentially severe and critical COVID-19 outcomes using an inexpensive, widely available, the point-of-care test has important practical implications for preventing mild patients from becoming severe, ef.....
    Document: Compared with traditional multi-stage methods, we demonstrate that our deep learning-based method can extract spatial and temporal information efficiently and improve the prediction performance significantly. The ability to identify patients with potentially severe and critical COVID-19 outcomes using an inexpensive, widely available, the point-of-care test has important practical implications for preventing mild patients from becoming severe, effectively improving cure rate, and reducing mortality. Our future work will focus on mining richer spatial information from the CT scan sequence and using AI technologies to screen the risk factors of potential severe/critical patients. author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

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