A prediction model of outcome of SARS-CoV-2 pneumonia based on laboratory findings

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has resulted in thousands of deaths in the world. Information about prediction model of prognosis of SARS-CoV-2 infection is scarce. We used machine learning for processing laboratory findings of 110 patients with SARS-CoV-2 pneumonia...

Full description

Saved in:
Bibliographic Details
Published in:Scientific reports 2020-08, Vol.10 (1), p.14042-14042, Article 14042
Main Authors: Wu, Gang, Zhou, Shuchang, Wang, Yujin, Lv, Wenzhi, Wang, Shili, Wang, Ting, Li, Xiaoming
Format: Article
Language:English
Subjects:
692/699/255
692/699/255/2514
Age
Aged
Algorithms
Artificial intelligence
Betacoronavirus - genetics
Bilirubin - blood
Blood
Blood platelets
Cochlea
Collagen
Coronavirus Infections - blood
Coronavirus Infections - therapy
Coronavirus Infections - virology
Coronaviruses
COVID-19
Cytokines
Data Accuracy
Degradation products
Dehydrogenases
Feasibility Studies
Feature selection
Female
Fibrin Fibrinogen Degradation Products - analysis
Forecasting - methods
Hearing
Hospitals
Humanities and Social Sciences
Humans
Infections
Laboratories
Leukocytes
Machine Learning
Male
Mapping
Models, Statistical
Mortality
multidisciplinary
Myoglobin - blood
Myoglobins
Neutrophils
Pandemics
Patients
Pneumonia
Pneumonia, Viral - blood
Pneumonia, Viral - therapy
Pneumonia, Viral - virology
Prediction models
Prognosis
Prothrombin - analysis
Receptors, Interleukin-2 - blood
Retrospective Studies
SARS-CoV-2
Science
Science (multidisciplinary)
Sensitivity and Specificity
Severe acute respiratory syndrome coronavirus 2
Treatment Outcome
Urea
Urea - blood
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
cited_by cdi_FETCH-LOGICAL-c527t-ff2720a820ed6c161e084a71dd991a6c82de42774322341bed18ce560c8190be3
cites cdi_FETCH-LOGICAL-c527t-ff2720a820ed6c161e084a71dd991a6c82de42774322341bed18ce560c8190be3
container_end_page 14042
container_issue 1
container_start_page 14042
container_title Scientific reports
container_volume 10
creator Wu, Gang
Zhou, Shuchang
Wang, Yujin
Lv, Wenzhi
Wang, Shili
Wang, Ting
Li, Xiaoming
description The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has resulted in thousands of deaths in the world. Information about prediction model of prognosis of SARS-CoV-2 infection is scarce. We used machine learning for processing laboratory findings of 110 patients with SARS-CoV-2 pneumonia (including 51 non-survivors and 59 discharged patients). The maximum relevance minimum redundancy (mRMR) algorithm and the least absolute shrinkage and selection operator logistic regression model were used for selection of laboratory features. Seven laboratory features selected in the model were: prothrombin activity, urea, white blood cell, interleukin-2 receptor, indirect bilirubin, myoglobin, and fibrinogen degradation products. The signature constructed using the seven features had 98% [93%, 100%] sensitivity and 91% [84%, 99%] specificity in predicting outcome of SARS-CoV-2 pneumonia. Thus it is feasible to establish an accurate prediction model of outcome of SARS-CoV-2 pneumonia based on laboratory findings.
doi_str_mv 10.1038/s41598-020-71114-7
format article
fullrecord <record><control><sourceid>proquest_pubme</sourceid><recordid>TN_cdi_pubmedcentral_primary_oai_pubmedcentral_nih_gov_7441177</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>4322133123</sourcerecordid><originalsourceid>FETCH-LOGICAL-c527t-ff2720a820ed6c161e084a71dd991a6c82de42774322341bed18ce560c8190be3</originalsourceid><addsrcrecordid>eNp1kU9r3DAQxUVJaUKaL9BDMeSSi1qNJFv2pbAs6R8IFJK2VyFL462CLW0kO5BvX203DZtAddHA_ObpjR4h74B9ACbaj1lC3bWUcUYVAEiqXpETzmRNueD86KA-Jmc537Jyat5J6N6QY8FbzjiwE3K9qrYJnbezj6GaosOxikMVl9nGCXflzer6hq7jL8qrbcBlisGbqjcZXVUmRtPHZOaYHqrBB-fDJr8lrwczZjx7vE_Jz8-XP9Zf6dX3L9_Wqytqa65mOgxccWaKEXSNhQaQtdIocK7rwDS25Q4lV0qWFYSEHh20FuuG2RY61qM4JZ_2utuln9BZDHMyo94mP5n0oKPx-nkn-N96E--1khJAqSJw8SiQ4t2CedaTzxbH0QSMS9ZcikbsfkwW9PwFehuXFMp6GtpOdI2Api4U31M2xZwTDk9mgOldanqfmi6p6b-p6Z2L94drPI38y6gAYg_k0gobTAdv_1_2D1vYoTM</addsrcrecordid><sourcetype>Open Access Repository</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>1893963165</pqid></control><display><type>article</type><title>A prediction model of outcome of SARS-CoV-2 pneumonia based on laboratory findings</title><source>Publicly Available Content Database</source><source>Full-Text Journals in Chemistry (Open access)</source><source>PubMed Central</source><source>Springer Nature - nature.com Journals - Fully Open Access</source><creator>Wu, Gang ; Zhou, Shuchang ; Wang, Yujin ; Lv, Wenzhi ; Wang, Shili ; Wang, Ting ; Li, Xiaoming</creator><creatorcontrib>Wu, Gang ; Zhou, Shuchang ; Wang, Yujin ; Lv, Wenzhi ; Wang, Shili ; Wang, Ting ; Li, Xiaoming</creatorcontrib><description>The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has resulted in thousands of deaths in the world. Information about prediction model of prognosis of SARS-CoV-2 infection is scarce. We used machine learning for processing laboratory findings of 110 patients with SARS-CoV-2 pneumonia (including 51 non-survivors and 59 discharged patients). The maximum relevance minimum redundancy (mRMR) algorithm and the least absolute shrinkage and selection operator logistic regression model were used for selection of laboratory features. Seven laboratory features selected in the model were: prothrombin activity, urea, white blood cell, interleukin-2 receptor, indirect bilirubin, myoglobin, and fibrinogen degradation products. The signature constructed using the seven features had 98% [93%, 100%] sensitivity and 91% [84%, 99%] specificity in predicting outcome of SARS-CoV-2 pneumonia. Thus it is feasible to establish an accurate prediction model of outcome of SARS-CoV-2 pneumonia based on laboratory findings.</description><identifier>ISSN: 2045-2322</identifier><identifier>EISSN: 2045-2322</identifier><identifier>DOI: 10.1038/s41598-020-71114-7</identifier><identifier>PMID: 32820210</identifier><language>eng</language><publisher>London: Nature Publishing Group UK</publisher><subject>692/699/255 ; 692/699/255/2514 ; Age ; Aged ; Algorithms ; Artificial intelligence ; Betacoronavirus - genetics ; Bilirubin - blood ; Blood ; Blood platelets ; Cochlea ; Collagen ; Coronavirus Infections - blood ; Coronavirus Infections - therapy ; Coronavirus Infections - virology ; Coronaviruses ; COVID-19 ; Cytokines ; Data Accuracy ; Degradation products ; Dehydrogenases ; Feasibility Studies ; Feature selection ; Female ; Fibrin Fibrinogen Degradation Products - analysis ; Forecasting - methods ; Hearing ; Hospitals ; Humanities and Social Sciences ; Humans ; Infections ; Laboratories ; Leukocytes ; Machine Learning ; Male ; Mapping ; Models, Statistical ; Mortality ; multidisciplinary ; Myoglobin - blood ; Myoglobins ; Neutrophils ; Pandemics ; Patients ; Pneumonia ; Pneumonia, Viral - blood ; Pneumonia, Viral - therapy ; Pneumonia, Viral - virology ; Prediction models ; Prognosis ; Prothrombin - analysis ; Receptors, Interleukin-2 - blood ; Retrospective Studies ; SARS-CoV-2 ; Science ; Science (multidisciplinary) ; Sensitivity and Specificity ; Severe acute respiratory syndrome coronavirus 2 ; Treatment Outcome ; Urea ; Urea - blood</subject><ispartof>Scientific reports, 2020-08, Vol.10 (1), p.14042-14042, Article 14042</ispartof><rights>The Author(s) 2020</rights><rights>The Author(s) 2020. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c527t-ff2720a820ed6c161e084a71dd991a6c82de42774322341bed18ce560c8190be3</citedby><cites>FETCH-LOGICAL-c527t-ff2720a820ed6c161e084a71dd991a6c82de42774322341bed18ce560c8190be3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.proquest.com/docview/1893963165/fulltextPDF?pq-origsite=primo$$EPDF$$P50$$Gproquest$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.proquest.com/docview/1893963165?pq-origsite=primo$$EHTML$$P50$$Gproquest$$Hfree_for_read</linktohtml><link.rule.ids>230,314,727,780,784,885,25752,27923,27924,37011,37012,44589,53790,53792,74997</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/32820210$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Wu, Gang</creatorcontrib><creatorcontrib>Zhou, Shuchang</creatorcontrib><creatorcontrib>Wang, Yujin</creatorcontrib><creatorcontrib>Lv, Wenzhi</creatorcontrib><creatorcontrib>Wang, Shili</creatorcontrib><creatorcontrib>Wang, Ting</creatorcontrib><creatorcontrib>Li, Xiaoming</creatorcontrib><title>A prediction model of outcome of SARS-CoV-2 pneumonia based on laboratory findings</title><title>Scientific reports</title><addtitle>Sci Rep</addtitle><addtitle>Sci Rep</addtitle><description>The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has resulted in thousands of deaths in the world. Information about prediction model of prognosis of SARS-CoV-2 infection is scarce. We used machine learning for processing laboratory findings of 110 patients with SARS-CoV-2 pneumonia (including 51 non-survivors and 59 discharged patients). The maximum relevance minimum redundancy (mRMR) algorithm and the least absolute shrinkage and selection operator logistic regression model were used for selection of laboratory features. Seven laboratory features selected in the model were: prothrombin activity, urea, white blood cell, interleukin-2 receptor, indirect bilirubin, myoglobin, and fibrinogen degradation products. The signature constructed using the seven features had 98% [93%, 100%] sensitivity and 91% [84%, 99%] specificity in predicting outcome of SARS-CoV-2 pneumonia. Thus it is feasible to establish an accurate prediction model of outcome of SARS-CoV-2 pneumonia based on laboratory findings.</description><subject>692/699/255</subject><subject>692/699/255/2514</subject><subject>Age</subject><subject>Aged</subject><subject>Algorithms</subject><subject>Artificial intelligence</subject><subject>Betacoronavirus - genetics</subject><subject>Bilirubin - blood</subject><subject>Blood</subject><subject>Blood platelets</subject><subject>Cochlea</subject><subject>Collagen</subject><subject>Coronavirus Infections - blood</subject><subject>Coronavirus Infections - therapy</subject><subject>Coronavirus Infections - virology</subject><subject>Coronaviruses</subject><subject>COVID-19</subject><subject>Cytokines</subject><subject>Data Accuracy</subject><subject>Degradation products</subject><subject>Dehydrogenases</subject><subject>Feasibility Studies</subject><subject>Feature selection</subject><subject>Female</subject><subject>Fibrin Fibrinogen Degradation Products - analysis</subject><subject>Forecasting - methods</subject><subject>Hearing</subject><subject>Hospitals</subject><subject>Humanities and Social Sciences</subject><subject>Humans</subject><subject>Infections</subject><subject>Laboratories</subject><subject>Leukocytes</subject><subject>Machine Learning</subject><subject>Male</subject><subject>Mapping</subject><subject>Models, Statistical</subject><subject>Mortality</subject><subject>multidisciplinary</subject><subject>Myoglobin - blood</subject><subject>Myoglobins</subject><subject>Neutrophils</subject><subject>Pandemics</subject><subject>Patients</subject><subject>Pneumonia</subject><subject>Pneumonia, Viral - blood</subject><subject>Pneumonia, Viral - therapy</subject><subject>Pneumonia, Viral - virology</subject><subject>Prediction models</subject><subject>Prognosis</subject><subject>Prothrombin - analysis</subject><subject>Receptors, Interleukin-2 - blood</subject><subject>Retrospective Studies</subject><subject>SARS-CoV-2</subject><subject>Science</subject><subject>Science (multidisciplinary)</subject><subject>Sensitivity and Specificity</subject><subject>Severe acute respiratory syndrome coronavirus 2</subject><subject>Treatment Outcome</subject><subject>Urea</subject><subject>Urea - blood</subject><issn>2045-2322</issn><issn>2045-2322</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><sourceid>PIMPY</sourceid><recordid>eNp1kU9r3DAQxUVJaUKaL9BDMeSSi1qNJFv2pbAs6R8IFJK2VyFL462CLW0kO5BvX203DZtAddHA_ObpjR4h74B9ACbaj1lC3bWUcUYVAEiqXpETzmRNueD86KA-Jmc537Jyat5J6N6QY8FbzjiwE3K9qrYJnbezj6GaosOxikMVl9nGCXflzer6hq7jL8qrbcBlisGbqjcZXVUmRtPHZOaYHqrBB-fDJr8lrwczZjx7vE_Jz8-XP9Zf6dX3L9_Wqytqa65mOgxccWaKEXSNhQaQtdIocK7rwDS25Q4lV0qWFYSEHh20FuuG2RY61qM4JZ_2utuln9BZDHMyo94mP5n0oKPx-nkn-N96E--1khJAqSJw8SiQ4t2CedaTzxbH0QSMS9ZcikbsfkwW9PwFehuXFMp6GtpOdI2Api4U31M2xZwTDk9mgOldanqfmi6p6b-p6Z2L94drPI38y6gAYg_k0gobTAdv_1_2D1vYoTM</recordid><startdate>20200820</startdate><enddate>20200820</enddate><creator>Wu, Gang</creator><creator>Zhou, Shuchang</creator><creator>Wang, Yujin</creator><creator>Lv, Wenzhi</creator><creator>Wang, Shili</creator><creator>Wang, Ting</creator><creator>Li, Xiaoming</creator><general>Nature Publishing Group UK</general><general>Nature Publishing Group</general><scope>C6C</scope><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7X7</scope><scope>7XB</scope><scope>88A</scope><scope>88E</scope><scope>88I</scope><scope>8FE</scope><scope>8FH</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABUWG</scope><scope>AEUYN</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BBNVY</scope><scope>BENPR</scope><scope>BHPHI</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>K9.</scope><scope>LK8</scope><scope>M0S</scope><scope>M1P</scope><scope>M2P</scope><scope>M7P</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>Q9U</scope><scope>7X8</scope><scope>5PM</scope></search><sort><creationdate>20200820</creationdate><title>A prediction model of outcome of SARS-CoV-2 pneumonia based on laboratory findings</title><author>Wu, Gang ; Zhou, Shuchang ; Wang, Yujin ; Lv, Wenzhi ; Wang, Shili ; Wang, Ting ; Li, Xiaoming</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c527t-ff2720a820ed6c161e084a71dd991a6c82de42774322341bed18ce560c8190be3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>692/699/255</topic><topic>692/699/255/2514</topic><topic>Age</topic><topic>Aged</topic><topic>Algorithms</topic><topic>Artificial intelligence</topic><topic>Betacoronavirus - genetics</topic><topic>Bilirubin - blood</topic><topic>Blood</topic><topic>Blood platelets</topic><topic>Cochlea</topic><topic>Collagen</topic><topic>Coronavirus Infections - blood</topic><topic>Coronavirus Infections - therapy</topic><topic>Coronavirus Infections - virology</topic><topic>Coronaviruses</topic><topic>COVID-19</topic><topic>Cytokines</topic><topic>Data Accuracy</topic><topic>Degradation products</topic><topic>Dehydrogenases</topic><topic>Feasibility Studies</topic><topic>Feature selection</topic><topic>Female</topic><topic>Fibrin Fibrinogen Degradation Products - analysis</topic><topic>Forecasting - methods</topic><topic>Hearing</topic><topic>Hospitals</topic><topic>Humanities and Social Sciences</topic><topic>Humans</topic><topic>Infections</topic><topic>Laboratories</topic><topic>Leukocytes</topic><topic>Machine Learning</topic><topic>Male</topic><topic>Mapping</topic><topic>Models, Statistical</topic><topic>Mortality</topic><topic>multidisciplinary</topic><topic>Myoglobin - blood</topic><topic>Myoglobins</topic><topic>Neutrophils</topic><topic>Pandemics</topic><topic>Patients</topic><topic>Pneumonia</topic><topic>Pneumonia, Viral - blood</topic><topic>Pneumonia, Viral - therapy</topic><topic>Pneumonia, Viral - virology</topic><topic>Prediction models</topic><topic>Prognosis</topic><topic>Prothrombin - analysis</topic><topic>Receptors, Interleukin-2 - blood</topic><topic>Retrospective Studies</topic><topic>SARS-CoV-2</topic><topic>Science</topic><topic>Science (multidisciplinary)</topic><topic>Sensitivity and Specificity</topic><topic>Severe acute respiratory syndrome coronavirus 2</topic><topic>Treatment Outcome</topic><topic>Urea</topic><topic>Urea - blood</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Wu, Gang</creatorcontrib><creatorcontrib>Zhou, Shuchang</creatorcontrib><creatorcontrib>Wang, Yujin</creatorcontrib><creatorcontrib>Lv, Wenzhi</creatorcontrib><creatorcontrib>Wang, Shili</creatorcontrib><creatorcontrib>Wang, Ting</creatorcontrib><creatorcontrib>Li, Xiaoming</creatorcontrib><collection>Springer Nature OA Free Journals</collection><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>ProQuest Health &amp; Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Biology Database (Alumni Edition)</collection><collection>Medical Database (Alumni Edition)</collection><collection>Science Database (Alumni Edition)</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Natural Science Collection</collection><collection>Hospital Premium Collection</collection><collection>Hospital Premium Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>ProQuest Central (Alumni)</collection><collection>ProQuest One Sustainability</collection><collection>ProQuest Central</collection><collection>ProQuest Central Essentials</collection><collection>Biological Science Collection</collection><collection>AUTh Library subscriptions: ProQuest Central</collection><collection>ProQuest Natural Science Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Central Student</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Health &amp; Medical Complete (Alumni)</collection><collection>ProQuest Biological Science Collection</collection><collection>Health &amp; Medical Collection (Alumni Edition)</collection><collection>PML(ProQuest Medical Library)</collection><collection>ProQuest Science Journals</collection><collection>Biological Science Database</collection><collection>Publicly Available Content Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central Basic</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>Scientific reports</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Wu, Gang</au><au>Zhou, Shuchang</au><au>Wang, Yujin</au><au>Lv, Wenzhi</au><au>Wang, Shili</au><au>Wang, Ting</au><au>Li, Xiaoming</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>A prediction model of outcome of SARS-CoV-2 pneumonia based on laboratory findings</atitle><jtitle>Scientific reports</jtitle><stitle>Sci Rep</stitle><addtitle>Sci Rep</addtitle><date>2020-08-20</date><risdate>2020</risdate><volume>10</volume><issue>1</issue><spage>14042</spage><epage>14042</epage><pages>14042-14042</pages><artnum>14042</artnum><issn>2045-2322</issn><eissn>2045-2322</eissn><abstract>The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has resulted in thousands of deaths in the world. Information about prediction model of prognosis of SARS-CoV-2 infection is scarce. We used machine learning for processing laboratory findings of 110 patients with SARS-CoV-2 pneumonia (including 51 non-survivors and 59 discharged patients). The maximum relevance minimum redundancy (mRMR) algorithm and the least absolute shrinkage and selection operator logistic regression model were used for selection of laboratory features. Seven laboratory features selected in the model were: prothrombin activity, urea, white blood cell, interleukin-2 receptor, indirect bilirubin, myoglobin, and fibrinogen degradation products. The signature constructed using the seven features had 98% [93%, 100%] sensitivity and 91% [84%, 99%] specificity in predicting outcome of SARS-CoV-2 pneumonia. Thus it is feasible to establish an accurate prediction model of outcome of SARS-CoV-2 pneumonia based on laboratory findings.</abstract><cop>London</cop><pub>Nature Publishing Group UK</pub><pmid>32820210</pmid><doi>10.1038/s41598-020-71114-7</doi><tpages>1</tpages><oa>free_for_read</oa></addata></record>
fulltext fulltext
identifier ISSN: 2045-2322
ispartof Scientific reports, 2020-08, Vol.10 (1), p.14042-14042, Article 14042
issn 2045-2322
2045-2322
language eng
recordid cdi_pubmedcentral_primary_oai_pubmedcentral_nih_gov_7441177
source Publicly Available Content Database; Full-Text Journals in Chemistry (Open access); PubMed Central; Springer Nature - nature.com Journals - Fully Open Access
subjects 692/699/255
692/699/255/2514
Age
Aged
Algorithms
Artificial intelligence
Betacoronavirus - genetics
Bilirubin - blood
Blood
Blood platelets
Cochlea
Collagen
Coronavirus Infections - blood
Coronavirus Infections - therapy
Coronavirus Infections - virology
Coronaviruses
COVID-19
Cytokines
Data Accuracy
Degradation products
Dehydrogenases
Feasibility Studies
Feature selection
Female
Fibrin Fibrinogen Degradation Products - analysis
Forecasting - methods
Hearing
Hospitals
Humanities and Social Sciences
Humans
Infections
Laboratories
Leukocytes
Machine Learning
Male
Mapping
Models, Statistical
Mortality
multidisciplinary
Myoglobin - blood
Myoglobins
Neutrophils
Pandemics
Patients
Pneumonia
Pneumonia, Viral - blood
Pneumonia, Viral - therapy
Pneumonia, Viral - virology
Prediction models
Prognosis
Prothrombin - analysis
Receptors, Interleukin-2 - blood
Retrospective Studies
SARS-CoV-2
Science
Science (multidisciplinary)
Sensitivity and Specificity
Severe acute respiratory syndrome coronavirus 2
Treatment Outcome
Urea
Urea - blood
title A prediction model of outcome of SARS-CoV-2 pneumonia based on laboratory findings
url http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-09T04%3A05%3A52IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_pubme&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=A%20prediction%20model%20of%20outcome%20of%20SARS-CoV-2%20pneumonia%20based%20on%20laboratory%20findings&rft.jtitle=Scientific%20reports&rft.au=Wu,%20Gang&rft.date=2020-08-20&rft.volume=10&rft.issue=1&rft.spage=14042&rft.epage=14042&rft.pages=14042-14042&rft.artnum=14042&rft.issn=2045-2322&rft.eissn=2045-2322&rft_id=info:doi/10.1038/s41598-020-71114-7&rft_dat=%3Cproquest_pubme%3E4322133123%3C/proquest_pubme%3E%3Cgrp_id%3Ecdi_FETCH-LOGICAL-c527t-ff2720a820ed6c161e084a71dd991a6c82de42774322341bed18ce560c8190be3%3C/grp_id%3E%3Coa%3E%3C/oa%3E%3Curl%3E%3C/url%3E&rft_id=info:oai/&rft_pqid=1893963165&rft_id=info:pmid/32820210&rfr_iscdi=true