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dc.creatorGao, Yue
dc.creatorCai, Guang-Yao
dc.creatorFang, Wei
dc.creatorLi, Hua-Yi
dc.creatorWang, Si-Yuan
dc.creatorChen, Lingxi
dc.creatorYu, Yang
dc.creatorLiu, Dan
dc.creatorXu, Sen
dc.creatorCui, Peng-Fei
dc.creatorZeng, Shao-Qing
dc.creatorFeng, Xin-Xia
dc.creatorYu, Rui-Di
dc.creatorWang, Ya
dc.creatorYuan, Yuan
dc.creatorJiao, Xiao-Fei
dc.creatorChi, Jian-Hua
dc.creatorLiu, Jia-Hao
dc.creatorLi, Ru-Yuan
dc.creatorZheng, Xu
dc.creatorSong, Chun-Yan
dc.creatorJin, Ning
dc.creatorGong, Wen-Jian
dc.creatorLiu, Xing-Yu
dc.creatorHuang, Lei
dc.creatorTian, Xun
dc.creatorLi, Lin
dc.creatorXing, Hui
dc.creatorMa, Ding
dc.creatorLi, Chun-Rui
dc.creatorYe, Fei
dc.creatorGao, Qing-Lei
dc.date.accessioned2020-10-13T20:53:05Z
dc.date.available2020-10-13T20:53:05Z
dc.date.created2020
dc.identifier.issn2041-1723spa
dc.identifier.otherhttps://doi.org/10.1038/s41467-020-18684-2spa
dc.identifier.urihttp://hdl.handle.net/20.500.12010/14433
dc.description.abstractSoaring cases of coronavirus disease (COVID-19) are pummeling the global health system. Overwhelmed health facilities have endeavored to mitigate the pandemic, but mortality of COVID-19 continues to increase. Here, we present a mortality risk prediction model for COVID-19 (MRPMC) that uses patients’ clinical data on admission to stratify patients by mortality risk, which enables prediction of physiological deterioration and death up to 20 days in advance. This ensemble model is built using four machine learning methods including Logistic Regression, Support Vector Machine, Gradient Boosted Decision Tree, and Neural Network. We validate MRPMC in an internal validation cohort and two external validation cohorts, where it achieves an AUC of 0.9621 (95% CI: 0.9464–0.9778), 0.9760 (0.9613–0.9906), and 0.9246 (0.8763–0.9729), respectively. This model enables expeditious and accurate mortality risk stratification of patients with COVID-19, and potentially facilitates more responsive health systems that are conducive to high risk COVID-19 patients.spa
dc.format.extent10 páginasspa
dc.format.mimetypeapplication/pdfspa
dc.language.isospaspa
dc.publisherNature communicationsspa
dc.sourcereponame:Expeditio Repositorio Institucional UJTLspa
dc.sourceinstname:Universidad de Bogotá Jorge Tadeo Lozanospa
dc.subjectMachine learningspa
dc.subjectEarly warning systemspa
dc.subjectMortality riskspa
dc.subjectCOVID-19spa
dc.subjectPredictionspa
dc.titleMachine learning based early warning system enables accurate mortality risk prediction for COVID-19spa
dc.type.localArtículospa
dc.subject.lembSíndrome respiratorio agudo gravespa
dc.subject.lembCOVID-19spa
dc.subject.lembSARS-CoV-2spa
dc.subject.lembCoronavirusspa
dc.rights.accessrightsinfo:eu-repo/semantics/openAccessspa
dc.type.hasversioninfo:eu-repo/semantics/acceptedVersionspa
dc.rights.localAbierto (Texto Completo)spa
dc.identifier.doihttps://doi.org/10.1038/s41467-020-18684-2spa
dc.type.coarhttp://purl.org/coar/resource_type/c_2df8fbb1spa


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