About: Early triage of critically ill COVID-19 patients using deep learning

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An Entity of Type : schema:ScholarlyArticle, within Data Space : wasabi.inria.fr associated with source document(s)

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
has title	Early triage of critically ill COVID-19 patients using deep learning
Creator	Wang, Wei Zhong, Nanshan Zhou, Rong Yang, Fan Liu, Jun Li, Yimin Chen, Guoqiang Guo, Haiyan Zhao, Yi Li, Shiyue Guo, Jun Han, Xiao Lu, Jiatao Lu, Ligong Lv, Qingquan Guan, Weijie He, Jianxing Liang, Wenhua Sang, Ling Xu, Yuanda Zanin, Mark Zhang, Nuofu Yao, Jianhua Chen, Ailan Chen, Hanbo Chen, Zisheng Huan, Wenjing Huang, Junzhou Liang, Hengrui Ou, Limin Tang, Weimin Wong, Sooksan Zhou, Niyun
Source	Medline; PMC
abstract	The sudden deterioration of patients with novel coronavirus disease 2019 (COVID-19) into critical illness is of major concern. It is imperative to identify these patients early. We show that a deep learning-based survival model can predict the risk of COVID-19 patients developing critical illness based on clinical characteristics at admission. We develop this model using a cohort of 1590 patients from 575 medical centers, with internal validation performance of concordance index 0.894 We further validate the model on three separate cohorts from Wuhan, Hubei and Guangdong provinces consisting of 1393 patients with concordance indexes of 0.890, 0.852 and 0.967 respectively. This model is used to create an online calculation tool designed for patient triage at admission to identify patients at risk of severe illness, ensuring that patients at greatest risk of severe illness receive appropriate care as early as possible and allow for effective allocation of health resources.
has issue date	2020-07-15(xsd:dateTime)
bibo:doi	10.1038/s41467-020-17280-8
bibo:pmid	32669540
has license	cc-by
sha1sum (hex)	9d30baea8e6f7099621a3e4119c6e75d933bc40e
schema:url	https://doi.org/10.1038/s41467-020-17280-8
resource representing a document's title	Early triage of critically ill COVID-19 patients using deep learning
has PubMed Central identifier	PMC7363899
has PubMed identifier	32669540
schema:publication	Nat Commun
resource representing a document's body	covid:9d30baea8e6f7099621a3e4119c6e75d933bc40e#body_text
is schema:about of	named entity 'coronavirus disease 2019' named entity 'predict' named entity 'severe' named entity 'patients' named entity 'severe' named entity 'deep learning' named entity 'INTERNAL' named entity 'CREATE' named entity 'DEEP LEARNING' named entity 'PREDICT' named entity 'COHORT' named entity 'RISK OF' named entity 'calculation' named entity 'COVID-19' named entity 'identify' named entity 'health' named entity 'validate' named entity 'Hubei' named entity 'COVID-19' named entity 'Wuhan' named entity 'coronavirus disease 2019' named entity 'NHC' named entity 'classification problem' named entity 'CoV' named entity 'neutrophil' named entity 'Wuhan' named entity 'deep learning' named entity 'statistically significant' named entity 'angiotensin-converting enzyme 2' named entity 'critical illness' named entity 'feedforward neural network' named entity 'overfitting' named entity 'follow-up' named entity 'Cox proportional hazards model' named entity 'missing data' named entity 'deep learning' named entity 'computed tomography' named entity 'dyspnea' named entity 'chest X-rays' named entity 'survival analysis' named entity 'comorbidity' named entity 'COVID-19 disease' named entity 'transcription' named entity 'high-throughput sequencing' named entity 'PCR' named entity 'clinical characteristics' named entity 'invasive ventilation' named entity 'hyperparameters' named entity 'LASSO'

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