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Machine Learning Algorithm Identifies Patients at High Risk for Early Complications After Intracranial Tumor Surgery: Registry-Based Cohort Study

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  • Additional Information
    • Publication Information:
      Oxford University Press
    • Publication Date:
      2019
    • Collection:
      University of Zurich (UZH): ZORA (Zurich Open Repository and Archive
    • Abstract:
      INTRODUCTION: Reliable preoperative identification of patients at high risk for early postoperative complications occurring within 24 h (EPC) of intracranial tumor surgery can improve patient safety and postoperative management. Statistical analysis using machine learning algorithms may generate models that predict EPC better than conventional statistical methods.
    • File Description:
      application/pdf
    • ISSN:
      0148-396X
    • Relation:
      https://www.zora.uzh.ch/id/eprint/172939/1/2019_Machine_Learning_Algorithm_Final_Paper_Neurosurgery.pdf; info:pmid/31149726; urn:issn:0148-396X
    • Accession Number:
      10.5167/uzh-172939
    • Accession Number:
      10.1093/neuros/nyz145
    • Online Access:
      https://doi.org/10.5167/uzh-17293910.1093/neuros/nyz145
      https://www.zora.uzh.ch/id/eprint/172939/
      https://www.zora.uzh.ch/id/eprint/172939/1/2019_Machine_Learning_Algorithm_Final_Paper_Neurosurgery.pdf
    • Rights:
      info:eu-repo/semantics/restrictedAccess
    • Accession Number:
      edsbas.BFD07067