View in EDS HTML Full Text PDF Full Text

Predictive Modeling of Urinary Stone Composition Using Machine Learning and Clinical Data: Implications for Treatment Strategies and Pathophysiological Insights.

Saved in:
Bibliographic Details
Title: Predictive Modeling of Urinary Stone Composition Using Machine Learning and Clinical Data: Implications for Treatment Strategies and Pathophysiological Insights.
Authors: Chmiel JA; Department of Microbiology and Immunology, Western University, London, Canada.; Canadian Centre for Human Microbiome and Probiotic Research, London, Canada., Stuivenberg GA; Department of Microbiology and Immunology, Western University, London, Canada.; Canadian Centre for Human Microbiome and Probiotic Research, London, Canada., Wong JFW; Division of Urology, Department of Surgery, Western University, London, Canada., Nott L; Division of Urology, Department of Surgery, Western University, London, Canada., Burton JP; Department of Microbiology and Immunology, Western University, London, Canada.; Canadian Centre for Human Microbiome and Probiotic Research, London, Canada.; Division of Urology, Department of Surgery, Western University, London, Canada., Razvi H; Division of Urology, Department of Surgery, Western University, London, Canada., Bjazevic J; Division of Urology, Department of Surgery, Western University, London, Canada.
Source: Journal of endourology [J Endourol] 2024 Aug; Vol. 38 (8), pp. 778-787. Date of Electronic Publication: 2024 May 30.
Publication Type: Journal Article
Journal Info: Publisher: Mary Ann Liebert Country of Publication: United States NLM ID: 8807503 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1557-900X (Electronic) Linking ISSN: 08927790 NLM ISO Abbreviation: J Endourol 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: 37975292
AccessLevel: 2
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
IllustrationInfo
Items – Name: Title
  Label: Title
  Group: Ti
  Data: Predictive Modeling of Urinary Stone Composition Using Machine Learning and Clinical Data: Implications for Treatment Strategies and Pathophysiological Insights.
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AU" term="%22Chmiel+JA%22">Chmiel JA</searchLink>; Department of Microbiology and Immunology, Western University, London, Canada.; Canadian Centre for Human Microbiome and Probiotic Research, London, Canada.<br /><searchLink fieldCode="AU" term="%22Stuivenberg+GA%22">Stuivenberg GA</searchLink>; Department of Microbiology and Immunology, Western University, London, Canada.; Canadian Centre for Human Microbiome and Probiotic Research, London, Canada.<br /><searchLink fieldCode="AU" term="%22Wong+JFW%22">Wong JFW</searchLink>; Division of Urology, Department of Surgery, Western University, London, Canada.<br /><searchLink fieldCode="AU" term="%22Nott+L%22">Nott L</searchLink>; Division of Urology, Department of Surgery, Western University, London, Canada.<br /><searchLink fieldCode="AU" term="%22Burton+JP%22">Burton JP</searchLink>; Department of Microbiology and Immunology, Western University, London, Canada.; Canadian Centre for Human Microbiome and Probiotic Research, London, Canada.; Division of Urology, Department of Surgery, Western University, London, Canada.<br /><searchLink fieldCode="AU" term="%22Razvi+H%22">Razvi H</searchLink>; Division of Urology, Department of Surgery, Western University, London, Canada.<br /><searchLink fieldCode="AU" term="%22Bjazevic+J%22">Bjazevic J</searchLink>; Division of Urology, Department of Surgery, Western University, London, Canada.
– Name: TitleSource
  Label: Source
  Group: Src
  Data: <searchLink fieldCode="JN" term="%228807503%22">Journal of endourology</searchLink> [J Endourol] 2024 Aug; Vol. 38 (8), pp. 778-787. <i>Date of Electronic Publication: </i>2024 May 30.
– Name: TypePub
  Label: Publication Type
  Group: TypPub
  Data: Journal Article
– Name: TitleSource
  Label: Journal Info
  Group: Src
  Data: <i>Publisher: </i><searchLink fieldCode="PB" term="%22Mary+Ann+Liebert%22">Mary Ann Liebert </searchLink><i>Country of Publication: </i>United States <i>NLM ID: </i>8807503 <i>Publication Model: </i>Print-Electronic <i>Cited Medium: </i>Internet <i>ISSN: </i>1557-900X (Electronic) <i>Linking ISSN: </i><searchLink fieldCode="IS" term="%2208927790%22">08927790 </searchLink><i>NLM ISO Abbreviation: </i>J Endourol <i>Subsets: </i>MEDLINE
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=mdl&AN=37975292
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1089/end.2023.0446
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        StartPage: 778
    Titles:
      – TitleFull: Predictive Modeling of Urinary Stone Composition Using Machine Learning and Clinical Data: Implications for Treatment Strategies and Pathophysiological Insights.
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Chmiel JA
      – PersonEntity:
          Name:
            NameFull: Stuivenberg GA
      – PersonEntity:
          Name:
            NameFull: Wong JFW
      – PersonEntity:
          Name:
            NameFull: Nott L
      – PersonEntity:
          Name:
            NameFull: Burton JP
      – PersonEntity:
          Name:
            NameFull: Razvi H
      – PersonEntity:
          Name:
            NameFull: Bjazevic J
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 01
              M: 08
              Text: 2024 Aug
              Type: published
              Y: 2024
          Identifiers:
            – Type: issn-electronic
              Value: 1557-900X
          Numbering:
            – Type: volume
              Value: 38
            – Type: issue
              Value: 8
          Titles:
            – TitleFull: Journal of endourology
              Type: main
ResultId 1