View in EDS HTML Full Text PDF Full Text

Predicting stroke severity of patients using interpretable machine learning algorithms.

Saved in:
Bibliographic Details
Title: Predicting stroke severity of patients using interpretable machine learning algorithms.
Authors: Sorayaie Azar A; SDU Health Informatics and Technology, The Maersk Mc-Kinney Moller Institute, University of Southern Denmark, Odense, Denmark.; Department of Computer Engineering, Urmia University, Urmia, Iran., Samimi T; Department of Health Information Technology, Urmia University of Medical Sciences, Urmia, Iran.; Health and Biomedical Informatics Research Center, Urmia University of Medical Sciences, Urmia, Iran., Tavassoli G; Department of Health Information Technology, Urmia University of Medical Sciences, Urmia, Iran.; Health and Biomedical Informatics Research Center, Urmia University of Medical Sciences, Urmia, Iran.; Department of Computer Engineering, Urmia Branch, Islamic Azad University, Urmia, Iran., Naemi A; SDU Health Informatics and Technology, The Maersk Mc-Kinney Moller Institute, University of Southern Denmark, Odense, Denmark., Rahimi B; Department of Health Information Technology, Urmia University of Medical Sciences, Urmia, Iran.; Health and Biomedical Informatics Research Center, Urmia University of Medical Sciences, Urmia, Iran., Hadianfard Z; Department of Health Information Technology, Urmia University of Medical Sciences, Urmia, Iran., Wiil UK; SDU Health Informatics and Technology, The Maersk Mc-Kinney Moller Institute, University of Southern Denmark, Odense, Denmark., Nazarbaghi S; Department of Neurology, School of Medicine, Urmia University of Medical Sciences, Urmia, Iran., Bagherzadeh Mohasefi J; SDU Health Informatics and Technology, The Maersk Mc-Kinney Moller Institute, University of Southern Denmark, Odense, Denmark. j.bagherzadeh@urmia.ac.ir.; Department of Computer Engineering, Urmia University, Urmia, Iran. j.bagherzadeh@urmia.ac.ir., Lotfnezhad Afshar H; Department of Health Information Technology, Urmia University of Medical Sciences, Urmia, Iran. lotfnezhadafshar.h@umsu.ac.ir.; Health and Biomedical Informatics Research Center, Urmia University of Medical Sciences, Urmia, Iran. lotfnezhadafshar.h@umsu.ac.ir.
Source: European journal of medical research [Eur J Med Res] 2024 Nov 14; Vol. 29 (1), pp. 547. Date of Electronic Publication: 2024 Nov 14.
Publication Type: Evaluation Study; Journal Article
Journal Info: Publisher: BioMed Central Country of Publication: England NLM ID: 9517857 Publication Model: Electronic Cited Medium: Internet ISSN: 2047-783X (Electronic) Linking ISSN: 09492321 NLM ISO Abbreviation: Eur J Med Res Subsets: MEDLINE
Database: MEDLINE Ultimate
Full text is not displayed to guests.
FullText Links:
  – Type: pdflink
Text:
  Availability: 1
Header DbId: mdl
DbLabel: MEDLINE Ultimate
An: 39538301
AccessLevel: 2
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
IllustrationInfo
Items – Name: Title
  Label: Title
  Group: Ti
  Data: Predicting stroke severity of patients using interpretable machine learning algorithms.
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AU" term="%22Sorayaie+Azar+A%22">Sorayaie Azar A</searchLink>; SDU Health Informatics and Technology, The Maersk Mc-Kinney Moller Institute, University of Southern Denmark, Odense, Denmark.; Department of Computer Engineering, Urmia University, Urmia, Iran.<br /><searchLink fieldCode="AU" term="%22Samimi+T%22">Samimi T</searchLink>; Department of Health Information Technology, Urmia University of Medical Sciences, Urmia, Iran.; Health and Biomedical Informatics Research Center, Urmia University of Medical Sciences, Urmia, Iran.<br /><searchLink fieldCode="AU" term="%22Tavassoli+G%22">Tavassoli G</searchLink>; Department of Health Information Technology, Urmia University of Medical Sciences, Urmia, Iran.; Health and Biomedical Informatics Research Center, Urmia University of Medical Sciences, Urmia, Iran.; Department of Computer Engineering, Urmia Branch, Islamic Azad University, Urmia, Iran.<br /><searchLink fieldCode="AU" term="%22Naemi+A%22">Naemi A</searchLink>; SDU Health Informatics and Technology, The Maersk Mc-Kinney Moller Institute, University of Southern Denmark, Odense, Denmark.<br /><searchLink fieldCode="AU" term="%22Rahimi+B%22">Rahimi B</searchLink>; Department of Health Information Technology, Urmia University of Medical Sciences, Urmia, Iran.; Health and Biomedical Informatics Research Center, Urmia University of Medical Sciences, Urmia, Iran.<br /><searchLink fieldCode="AU" term="%22Hadianfard+Z%22">Hadianfard Z</searchLink>; Department of Health Information Technology, Urmia University of Medical Sciences, Urmia, Iran.<br /><searchLink fieldCode="AU" term="%22Wiil+UK%22">Wiil UK</searchLink>; SDU Health Informatics and Technology, The Maersk Mc-Kinney Moller Institute, University of Southern Denmark, Odense, Denmark.<br /><searchLink fieldCode="AU" term="%22Nazarbaghi+S%22">Nazarbaghi S</searchLink>; Department of Neurology, School of Medicine, Urmia University of Medical Sciences, Urmia, Iran.<br /><searchLink fieldCode="AU" term="%22Bagherzadeh+Mohasefi+J%22">Bagherzadeh Mohasefi J</searchLink>; SDU Health Informatics and Technology, The Maersk Mc-Kinney Moller Institute, University of Southern Denmark, Odense, Denmark. j.bagherzadeh@urmia.ac.ir.; Department of Computer Engineering, Urmia University, Urmia, Iran. j.bagherzadeh@urmia.ac.ir.<br /><searchLink fieldCode="AU" term="%22Lotfnezhad+Afshar+H%22">Lotfnezhad Afshar H</searchLink>; Department of Health Information Technology, Urmia University of Medical Sciences, Urmia, Iran. lotfnezhadafshar.h@umsu.ac.ir.; Health and Biomedical Informatics Research Center, Urmia University of Medical Sciences, Urmia, Iran. lotfnezhadafshar.h@umsu.ac.ir.
– Name: TitleSource
  Label: Source
  Group: Src
  Data: <searchLink fieldCode="JN" term="%229517857%22">European journal of medical research</searchLink> [Eur J Med Res] 2024 Nov 14; Vol. 29 (1), pp. 547. <i>Date of Electronic Publication: </i>2024 Nov 14.
– Name: TypePub
  Label: Publication Type
  Group: TypPub
  Data: Evaluation Study; Journal Article
– Name: TitleSource
  Label: Journal Info
  Group: Src
  Data: <i>Publisher: </i><searchLink fieldCode="PB" term="%22BioMed+Central%22">BioMed Central </searchLink><i>Country of Publication: </i>England <i>NLM ID: </i>9517857 <i>Publication Model: </i>Electronic <i>Cited Medium: </i>Internet <i>ISSN: </i>2047-783X (Electronic) <i>Linking ISSN: </i><searchLink fieldCode="IS" term="%2209492321%22">09492321 </searchLink><i>NLM ISO Abbreviation: </i>Eur J Med Res <i>Subsets: </i>MEDLINE
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=mdl&AN=39538301
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1186/s40001-024-02147-1
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        StartPage: 547
    Titles:
      – TitleFull: Predicting stroke severity of patients using interpretable machine learning algorithms.
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Sorayaie Azar A
      – PersonEntity:
          Name:
            NameFull: Samimi T
      – PersonEntity:
          Name:
            NameFull: Tavassoli G
      – PersonEntity:
          Name:
            NameFull: Naemi A
      – PersonEntity:
          Name:
            NameFull: Rahimi B
      – PersonEntity:
          Name:
            NameFull: Hadianfard Z
      – PersonEntity:
          Name:
            NameFull: Wiil UK
      – PersonEntity:
          Name:
            NameFull: Nazarbaghi S
      – PersonEntity:
          Name:
            NameFull: Bagherzadeh Mohasefi J
      – PersonEntity:
          Name:
            NameFull: Lotfnezhad Afshar H
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 14
              M: 11
              Text: 2024 Nov 14
              Type: published
              Y: 2024
          Identifiers:
            – Type: issn-electronic
              Value: 2047-783X
          Numbering:
            – Type: volume
              Value: 29
            – Type: issue
              Value: 1
          Titles:
            – TitleFull: European journal of medical research
              Type: main
ResultId 1