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


van Niftrik, Christiaan H B; van der Wouden, Frank; Staartjes, Victor E; Fierstra, Jorn; Stienen, Martin N; Akeret, Kevin; Sebök, Martina; Fedele, Tommaso; Sarnthein, Johannes; Bozinov, Oliver; Krayenbühl, Niklaus; Regli, Luca; Serra, Carlo (2019). Machine Learning Algorithm Identifies Patients at High Risk for Early Complications After Intracranial Tumor Surgery: Registry-Based Cohort Study. Neurosurgery, 85(4):E756-E764.

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.

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.

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Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:04 Faculty of Medicine > University Hospital Zurich > Clinic for Neurosurgery
Dewey Decimal Classification:610 Medicine & health
Scopus Subject Areas:Health Sciences > Surgery
Health Sciences > Neurology (clinical)
Uncontrolled Keywords:Brain tumor; Complication; Machine learning algorithm; Neurocritical care; Neurosurgery; Prediction model - Surgery, Clinical Neurology
Language:English
Date:1 October 2019
Deposited On:20 Aug 2019 13:36
Last Modified:29 Jul 2020 11:09
Publisher:Oxford University Press
ISSN:0148-396X
OA Status:Closed
Publisher DOI:https://doi.org/10.1093/neuros/nyz145
PubMed ID:31149726

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