Machine learning based on clinical characteristics and chest CT quantitative measurements for prediction of adverse clinical outcomes in hospitalized patients with COVID-19.
Zhichao FengHui ShenKai GaoJianpo SuShanhu YaoQin LiuZhimin YanJunhong DuanDali YiHuafei ZhaoHuiling LiQizhi YuWenming ZhouXiaowen MaoXin OuyangJi MeiQiuhua ZengLindy WilliamsXiaoqian MaPengfei RongDewen HuWei WangPublished in: European radiology (2021)
• Developing an individually prognostic model for COVID-19 has the potential to allow efficient allocation of medical resources. • We proposed a deep learning-based framework for accurate lung involvement quantification on chest CT images. • Machine learning based on clinical and CT variables can facilitate the prediction of adverse outcomes of COVID-19.
Keyphrases
- machine learning
- deep learning
- image quality
- coronavirus disease
- dual energy
- computed tomography
- sars cov
- contrast enhanced
- artificial intelligence
- positron emission tomography
- convolutional neural network
- high resolution
- magnetic resonance imaging
- big data
- emergency department
- mass spectrometry
- human health
- adverse drug