Development of machine learning models to prognosticate chronic shunt-dependent hydrocephalus after aneurysmal subarachnoid hemorrhage.

Journal: Acta neurochirurgica
Published Date: Dec 1, 2020

Abstract

BACKGROUND: Shunt-dependent hydrocephalus significantly complicates subarachnoid hemorrhage (SAH), and reliable prognosis methods have been sought in recent years to reduce morbidity and costs associated with delayed treatment or neglected onset. Machine learning (ML) defines modern data analysis techniques allowing accurate subject-based risk stratifications. We aimed at developing and testing different ML models to predict shunt-dependent hydrocephalus after aneurysmal SAH.

Authors

  • Giovanni Muscas
    2Department of Neurosurgery, Tuscany School of Neurosurgery, University of Firenze, Italy; and.
  • Tommaso Matteuzzi
    Institute of Physics, Alma Mater Studiorum, University of Bologna, Bologna, Italy.
  • Eleonora Becattini
    Neurosurgery Clinic, Department of Neuroscience, Psychology, Pharmacology and Child Health, Careggi University Hospital and University of Florence, Largo Piero Palagi 1, 50137, Florence, Italy.
  • Simone Orlandini
    Neurosurgery Clinic, Department of Neuroscience, Psychology, Pharmacology and Child Health, Careggi University Hospital and University of Florence, Largo Piero Palagi 1, 50137, Florence, Italy.
  • Francesca Battista
    Neurosurgery Clinic, Department of Neuroscience, Psychology, Pharmacology and Child Health, Careggi University Hospital and University of Florence, Largo Piero Palagi 1, 50137, Florence, Italy.
  • Antonio Laiso
    Neurosurgery Clinic, Department of Neuroscience, Psychology, Pharmacology and Child Health, Careggi University Hospital and University of Florence, Largo Piero Palagi 1, 50137, Florence, Italy.
  • Sergio Nappini
    Interventional Neuroradiology Unit, Department of Neuroscience, Psychology, Pharmacology and Child Health, Careggi University Hospital and University of Florence, Florence, Italy.
  • Nicola Limbucci
    Interventional Neuroradiology Unit, Department of Neuroscience, Psychology, Pharmacology and Child Health, Careggi University Hospital and University of Florence, Florence, Italy.
  • Leonardo Renieri
    Interventional Neuroradiology Unit, Department of Neuroscience, Psychology, Pharmacology and Child Health, Careggi University Hospital and University of Florence, Florence, Italy.
  • Biagio R Carangelo
    Department of Neurosurgery, Le Scotte University Hospital, Siena, Italy.
  • Salvatore Mangiafico
    Interventional Neuroradiology Unit, Department of Neuroscience, Psychology, Pharmacology and Child Health, Careggi University Hospital and University of Florence, Florence, Italy.
  • Alessandro Della Puppa
    Neurosurgery Clinic, Department of Neuroscience, Psychology, Pharmacology and Child Health, Careggi University Hospital and University of Florence, Largo Piero Palagi 1, 50137, Florence, Italy.

Keywords

External Resources

Popular Topics
Recent Journals