Erzincan Binali Yıldırım ÜniversitesiErzincan Binali Yıldırım ÜniversitesiAçık Erişim Arşivi · RDL Open

Machine learning risk prediction of mortality for patients undergoing surgery with perioperative SARS-CoV-2: The COVIDSurg mortality score

ArticleAçık Erişim

Anahtar Kelimeler

algorithmArticlecohort analysiscontrolled studycoronavirus disease 2019diagnostic test accuracy studyfeature selectionhumanmachine learningmortalitymulticenter studyoutcome assessmentpredictionprospective studyreceiver operating characteristicriskSevere acute respiratory syndrome coronavirus 2validation study
RDL Kalıcı Bağlantı (PID)
https://rdlopen.rawdatalibrary.net/r/ebyu/13400Resmi atıflarda kullanılan kalıcı adres
DOI
https://doi.org/10.1093/bjs/znab183Yayıncı resmi DOI adresi
Tam Kayıt
AlanDeğerDil
dc.contributor.authorBravo, Laura
dc.contributor.authorNepogodiev, Dmitri
dc.contributor.authorGlasbey, James C.
dc.contributor.authorLi, Elizabeth
dc.contributor.authorSimoes, Joana F.F.
dc.contributor.authorKamarajah, Sivesh K.
dc.contributor.authorPicciochi, Maria
dc.contributor.authorAbbott, Tom E.F.
dc.contributor.authorAdemuyiwa, Adesoji O.
dc.contributor.authorArnaud, Alexis P.
dc.contributor.authorAgarwal, Arnav
dc.contributor.authorBrar, Amanpreet
dc.contributor.authorElhadi, Muhammed
dc.contributor.authorMazingi, Dennis
dc.contributor.authorCardoso, Victor Roth
dc.contributor.authorLawday, Samuel
dc.contributor.authorSayyed, Raza
dc.contributor.authorOmar, Omar M.
dc.contributor.authorde la Madina, Antonio Ramos
dc.contributor.authorSlater, Luke
dc.contributor.authorVenn, Mary L.
dc.contributor.authorGkoutos, Georgios
dc.contributor.authorBhangu, Aneel
dc.contributor.authorKarwath, Andreas
dc.contributor.authorSiaw-Acheampong, Kwabena
dc.contributor.authorArgus, Leah
dc.contributor.authorChaudhry, Daoud
dc.contributor.authorDawson, Brett E.
dc.contributor.authorGujjuri, Rohan R.
dc.contributor.authorJones, Conor S.
dc.contributor.authorKhatri, Chetan
dc.contributor.authorKeatley, James M.
dc.contributor.authorMann, Harvinder
dc.contributor.authorMarson, Ella J.
dc.contributor.authorMclean, Kenneth A.
dc.contributor.authorTaylor, Elliott H.
dc.contributor.authorTiwari, Abhinav
dc.contributor.authorTrout, Isobel M.
dc.contributor.authorWilkin, R.
dc.contributor.authorDajti, Irida
dc.contributor.authorGjata, Arben
dc.contributor.authorBoccalatte, Luis
dc.contributor.authorModolo, Maria Marta
dc.contributor.authorCox, Daniel
dc.contributor.authorPockney, Peter
dc.contributor.authorTownend, Philip
dc.contributor.authorAigner, Felix
dc.contributor.authorKronberger, Irmgard
dc.contributor.authorHossain, Kamral
dc.contributor.authorVanRamshorst, Gabrielle
dc.date.accessioned2026-10-18T20:28:14.186310190Z
dc.date.available2026-10-18T20:28:14.186310190Z
dc.date.issued2021
dc.description.sponsorshipThis report was funded by a National Institute for Health Research (NIHR) Global Health Research Unit Grant (NIHR 16.136.79), Association of Coloproctology of Great Britain and Ireland, Bowel & Cancer Research, Bowel Research UK, Association of Upper Gastrointestinal Surgeons, British Association of Surgical Oncology, British Gynaecological Cancer Society, European Society of Coloproctology, Medtronic, NIHR Academy, The Urology Foundation, Sarcoma UK, Vascular Society for Great Britain and Ireland, Yorkshire Cancer Research, and the MRC Health Data Research UK (HDRUK/CFC/01), an initiative funded by UK Research and Innovation, Department of Health and Social Care (England) and the devolved administrations, and leading medical research charities. L.B.M. and G.V.G. also acknowledge the Wellcome Trust 4-year studentship programme in mechanisms of inflammatory disease (MIDAS; 215182/Z/19/Z). The funders had no role in study design, data collection, analysis and interpretation, or writing of this report. The views expressed are those of the authors and not necessarily those of the National Health Service, the NIHR, or the UK Department of Health and Social Care.
dc.identifier.doi10.1093/bjs/znab183
dc.identifier.issn0007-1323
dc.identifier.issue4
dc.identifier.pmid34227657
dc.identifier.scopus2-s2.0-85113431484
dc.identifier.startpageznab183
dc.identifier.urihttps://rdlopen.rawdatalibrary.net/r/ebyu/13400
dc.identifier.volume19
dc.language.isoen
dc.publisherOxford University Press
dc.relation.ispartofBritish Journal of Surgery
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectalgorithm
dc.subjectArticle
dc.subjectcohort analysis
dc.subjectcontrolled study
dc.subjectcoronavirus disease 2019
dc.subjectdiagnostic test accuracy study
dc.subjectfeature selection
dc.subjecthuman
dc.subjectmachine learning
dc.subjectmortality
dc.subjectmulticenter study
dc.subjectoutcome assessment
dc.subjectprediction
dc.subjectprospective study
dc.subjectreceiver operating characteristic
dc.subjectrisk
dc.subjectSevere acute respiratory syndrome coronavirus 2
dc.subjectvalidation study
dc.titleMachine learning risk prediction of mortality for patients undergoing surgery with perioperative SARS-CoV-2: The COVIDSurg mortality score
dc.typeArticle
dspace.entity.typePublication
rdl.citation.scopus25
rdl.oa.statusgold
rdl.source.indexSCOPUS