Machine learning based on clinical characteristics and chest CT quantitative measurements for prediction of adverse clinical outcomes in hospitalized patients with COVID-19

In this study, the authors aimed to develop and validate a machine learning model for the prediction of adverse outcomes in hospitalized patients with COVID-19. They discovered that their findings could be used to facilitate the prediction of adverse outcomes in patients with COVID-19, as well as may allow efficient utilization of medical resources and individualized treatment plans for COVID-19 patients. Key points 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. Article: Machine learning based on clinical characteristics and chest CT quantitative measurements for prediction of adverse clinical outcomes in hospitalized patients with COVID-19 Authors: Zhichao Feng, Hui Shen, Kai Gao, Jianpo Su, Shanhu Yao, Qin Liu, Zhimin Yan, Junhong Duan, Dali Yi, Huafei Zhao, Huiling Li, Qizhi Yu, Wenming Zhou, Xiaowen Mao, Xin Ouyang, Ji Mei, Qiuhua Zeng, Lindy Williams, Xiaoqian Ma, Pengfei Rong, Dewen Hu & Wei Wang