tetano
Editor, Senior Moderator
Nat Biomed Eng
. 2020 Nov 18.
doi: 10.1038/s41551-020-00633-5. Online ahead of print.
Open resource of clinical data from patients with pneumonia for the prediction of COVID-19 outcomes via deep learning
Wanshan Ning[SUP] 1 [/SUP], Shijun Lei[SUP] 2 3 [/SUP], Jingjing Yang[SUP] 4 5 [/SUP], Yukun Cao[SUP] 6 7 [/SUP], Peiran Jiang[SUP] 1 [/SUP], Qianqian Yang[SUP] 2 [/SUP], Jiao Zhang[SUP] 2 [/SUP], Xiaobei Wang[SUP] 2 [/SUP], Fenghua Chen[SUP] 2 [/SUP], Zhi Geng[SUP] 2 [/SUP], Liang Xiong[SUP] 8 [/SUP], Hongmei Zhou[SUP] 9 [/SUP], Yaping Guo[SUP] 1 [/SUP], Yulan Zeng[SUP] 10 [/SUP], Heshui Shi[SUP] 11 12 [/SUP], Lin Wang[SUP] 13 14 [/SUP], Yu Xue[SUP] 15 [/SUP], Zheng Wang[SUP] 16 17 [/SUP]
Affiliations
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.
. 2020 Nov 18.
doi: 10.1038/s41551-020-00633-5. Online ahead of print.
Open resource of clinical data from patients with pneumonia for the prediction of COVID-19 outcomes via deep learning
Wanshan Ning[SUP] 1 [/SUP], Shijun Lei[SUP] 2 3 [/SUP], Jingjing Yang[SUP] 4 5 [/SUP], Yukun Cao[SUP] 6 7 [/SUP], Peiran Jiang[SUP] 1 [/SUP], Qianqian Yang[SUP] 2 [/SUP], Jiao Zhang[SUP] 2 [/SUP], Xiaobei Wang[SUP] 2 [/SUP], Fenghua Chen[SUP] 2 [/SUP], Zhi Geng[SUP] 2 [/SUP], Liang Xiong[SUP] 8 [/SUP], Hongmei Zhou[SUP] 9 [/SUP], Yaping Guo[SUP] 1 [/SUP], Yulan Zeng[SUP] 10 [/SUP], Heshui Shi[SUP] 11 12 [/SUP], Lin Wang[SUP] 13 14 [/SUP], Yu Xue[SUP] 15 [/SUP], Zheng Wang[SUP] 16 17 [/SUP]
Affiliations
- PMID: 33208927
- DOI: 10.1038/s41551-020-00633-5
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.