Development and validation of an interpretable machine learning model for retrospective identification of suspected infection for sepsis surveillance: a multicentre cohort study.
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| Title: | Development and validation of an interpretable machine learning model for retrospective identification of suspected infection for sepsis surveillance: a multicentre cohort study. |
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| Authors: | Tuinte RAM; Department of Internal Medicine, Radboud University Medical Center, Nijmegen, the Netherlands.; Radboud Community for Infectious Diseases, Radboud University Medical Center, Nijmegen, the Netherlands., Smolenaers LPJ; Department of Internal Medicine, Radboud University Medical Center, Nijmegen, the Netherlands.; Radboud Community for Infectious Diseases, Radboud University Medical Center, Nijmegen, the Netherlands.; Tilburg School of Economics and Management, Tilburg University, the Netherlands.; AethiQs B.V., the Netherlands., Knoop BT; Department of Internal Medicine, Radboud University Medical Center, Nijmegen, the Netherlands.; Radboud Community for Infectious Diseases, Radboud University Medical Center, Nijmegen, the Netherlands., Föhse K; Department of Internal Medicine, Radboud University Medical Center, Nijmegen, the Netherlands.; Radboud Community for Infectious Diseases, Radboud University Medical Center, Nijmegen, the Netherlands., van der Aart TJ; Department of Acute Care, University Medical Center Groningen, University of Groningen, Groningen, the Netherlands.; Department of Internal Medicine, University Medical Center Groningen, University of Groningen, Groningen, the Netherlands., Bouma HR; Department of Acute Care, University Medical Center Groningen, University of Groningen, Groningen, the Netherlands.; Department of Internal Medicine, University Medical Center Groningen, University of Groningen, Groningen, the Netherlands.; Department of Clinical Pharmacy & Pharmacology, University Medical Center Groningen, University of Groningen, Groningen, the Netherlands., Netea MG; Department of Internal Medicine, Radboud University Medical Center, Nijmegen, the Netherlands.; Radboud Community for Infectious Diseases, Radboud University Medical Center, Nijmegen, the Netherlands.; Department of Immunology and Metabolism, Life and Medical Sciences Institute, University of Bonn, Bonn, Germany., Van Deun K; Department of Methodology & Statistics, Tilburg University, the Netherlands., Ten Oever J; Department of Internal Medicine, Radboud University Medical Center, Nijmegen, the Netherlands.; Radboud Community for Infectious Diseases, Radboud University Medical Center, Nijmegen, the Netherlands., Hoogerwerf JJ; Department of Internal Medicine, Radboud University Medical Center, Nijmegen, the Netherlands.; Radboud Community for Infectious Diseases, Radboud University Medical Center, Nijmegen, the Netherlands. |
| Source: | EClinicalMedicine [EClinicalMedicine] 2025 Aug 08; Vol. 87, pp. 103401. Date of Electronic Publication: 2025 Aug 08 (Print Publication: 2025). |
| Publication Type: | Journal Article |
| Journal Info: | Publisher: The Lancet Country of Publication: England NLM ID: 101733727 Publication Model: eCollection Cited Medium: Internet ISSN: 2589-5370 (Electronic) Linking ISSN: 25895370 NLM ISO Abbreviation: EClinicalMedicine Subsets: PubMed not MEDLINE |
| Database: | MEDLINE Ultimate |
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