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Open resource of clinical data from patients with pneumonia for the prediction of COVID-19 outcomes via deep learning.
Ning, Wanshan; Lei, Shijun; Yang, Jingjing; Cao, Yukun; Jiang, Peiran; Yang, Qianqian; Zhang, Jiao; Wang, Xiaobei; Chen, Fenghua; Geng, Zhi; Xiong, Liang; Zhou, Hongmei; Guo, Yaping; Zeng, Yulan; Shi, Heshui; Wang, Lin; Xue, Yu; Wang, Zheng.
  • Ning W; Key Laboratory of Molecular Biophysics of Ministry of Education, Hubei Bioinformatics and Molecular Imaging Key Laboratory, Center for Artificial Intelligence Biology, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, China.
  • Lei S; Department of Clinical Laboratory, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
  • Yang J; Research Center for Tissue Engineering and Regenerative Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
  • Cao Y; Department of Respiratory and Critical Care Medicine, Liyuan Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
  • Jiang P; Department of Critical Care Medicine, Renmin Hospital of Wuhan University, Wuhan, China.
  • Yang Q; Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
  • Zhang J; Hubei Province Key Laboratory of Molecular Imaging, Wuhan, China.
  • Wang X; Key Laboratory of Molecular Biophysics of Ministry of Education, Hubei Bioinformatics and Molecular Imaging Key Laboratory, Center for Artificial Intelligence Biology, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, China.
  • Chen F; Department of Clinical Laboratory, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
  • Geng Z; Department of Clinical Laboratory, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
  • Xiong L; Department of Clinical Laboratory, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
  • Zhou H; Department of Clinical Laboratory, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
  • Guo Y; Department of Clinical Laboratory, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
  • Zeng Y; Department of Laboratory Medicine, Liyuan Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
  • Shi H; Department of Radiology, Liyuan Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
  • Wang L; Key Laboratory of Molecular Biophysics of Ministry of Education, Hubei Bioinformatics and Molecular Imaging Key Laboratory, Center for Artificial Intelligence Biology, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, China.
  • Xue Y; Department of Respiratory and Critical Care Medicine, Liyuan Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China. 1989ly0551@hust.edu.cn.
  • Wang Z; Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China. heshuishi@hust.edu.cn.
Nat Biomed Eng ; 4(12): 1197-1207, 2020 12.
Article in English | MEDLINE | ID: covidwho-933689
ABSTRACT
Data from patients with coronavirus disease 2019 (COVID-19) are essential for guiding clinical decision making, for furthering the understanding of this viral disease, and for diagnostic modelling. Here, we describe an open resource containing data from 1,521 patients with pneumonia (including COVID-19 pneumonia) consisting of chest computed tomography (CT) images, 130 clinical features (from a range of biochemical and cellular analyses of blood and urine samples) and laboratory-confirmed severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) clinical status. We show the utility of the database for prediction of COVID-19 morbidity and mortality outcomes using a deep learning algorithm trained with data from 1,170 patients and 19,685 manually labelled CT slices. In an independent validation cohort of 351 patients, the algorithm discriminated between negative, mild and severe cases with areas under the receiver operating characteristic curve of 0.944, 0.860 and 0.884, respectively. The open database may have further uses in the diagnosis and management of patients with COVID-19.
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Full text: Available Collection: International databases Database: MEDLINE Main subject: Pneumonia, Viral / COVID-19 Type of study: Cohort study / Observational study / Prognostic study Limits: Female / Humans / Male Language: English Journal: Nat Biomed Eng Year: 2020 Document Type: Article Affiliation country: S41551-020-00633-5

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Full text: Available Collection: International databases Database: MEDLINE Main subject: Pneumonia, Viral / COVID-19 Type of study: Cohort study / Observational study / Prognostic study Limits: Female / Humans / Male Language: English Journal: Nat Biomed Eng Year: 2020 Document Type: Article Affiliation country: S41551-020-00633-5