An interpretable mortality prediction model for COVID-19 patients

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dc.creatorZhang, Mingyang
dc.creatorHuang, Xiang
dc.creatorXiao, Ying
dc.creatorCao, Haosen
dc.creatorChen, Yanyan
dc.creatorRen, Tongxin
dc.creatorWang, Fang
dc.creatorXiao, Yaru
dc.creatorHuang, Sufang
dc.creatorTan, Xi
dc.creatorYan, Li
dc.creatorZhang, Hai-Tao
dc.creatorGoncalves, Jorge
dc.creatorXiao, Yang
dc.creatorWang, Maolin
dc.creatorGuo, Yuqi
dc.creatorSun, Chuan
dc.creatorTang, Xiuchuan
dc.creatorJing, Liang
dc.creatorHuang, Niannian
dc.creatorJiao, Bo
dc.creatorCheng, Cheng
dc.creatorZhang, Yong
dc.creatorLuo, Ailin
dc.creatorMombaerts, Laurent
dc.creatorJin, Junyang
dc.creatorCao, Zhiguo
dc.creatorLi, Shusheng
dc.creatorXu, Hui
dc.creatorYuan, Ye
dc.date.accessioned2020-07-16T17:08:32Z
dc.date.available2020-07-16T17:08:32Z
dc.date.created2020-05-14
dc.description.abstractenglishThe sudden increase in COVID-19 cases is putting high pressure on healthcare services worldwide. At this stage, fast, accurate and early clinical assessment of the disease severity is vital. To support decision making and logistical planning in healthcare systems, this study leverages a database of blood samples from 485 infected patients in the region of Wuhan, China, to identify crucial predictive biomarkers of disease mortality. For this purpose, machine learning tools selected three biomarkers that predict the mortality of individual patients more than 10 days in advance with more than 90% accuracy: lactic dehydrogenase (LDH), lymphocyte and high-sensitivity C-reactive protein (hs-CRP). In particular, relatively high levels of LDH alone seem to play a crucial role in distinguishing the vast majority of cases that require immediate medical attention. This finding is consistent with current medical knowledge that high LDH levels are associated with tissue breakdown occurring in various diseases, including pulmonary disorders such as pneumonia. Overall, this Article suggests a simple and operable decision rule to quickly predict patients at the highest risk, allowing them to be prioritized and potentially reducing the mortality rate.spa
dc.format.extent8 páginasspa
dc.format.mimetypeapplication/pdfspa
dc.identifier.doihttps://doi.org/10.1038/s42256-020-0180-7spa
dc.identifier.issn2522-5839spa
dc.identifier.otherhttps://www.nature.com/articles/s42256-020-0180-7spa
dc.identifier.urihttps://hdl.handle.net/20.500.12010/10675
dc.publisherScience Directeng
dc.rights.accessrightsinfo:eu-repo/semantics/openAccessspa
dc.sourcereponame:Expeditio Repositorio Institucional UJTLspa
dc.sourceinstname:Universidad de Bogotá Jorge Tadeo Lozanospa
dc.subjectMortalidadspa
dc.subject.keywordMortalityspa
dc.subject.lembSíndrome respiratorio agudo gravespa
dc.subject.lembCOVID-19spa
dc.subject.lembSARS-CoV-2spa
dc.subject.lembCoronavirusspa
dc.titleAn interpretable mortality prediction model for COVID-19 patientsspa
dc.type.hasversioninfo:eu-repo/semantics/acceptedVersionspa
dc.type.localArtículospa

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