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

• Published in: Acta neurochirurgica Vol. 162; no. 12; pp. 3093 - 3105
• Main Authors: Muscas, Giovanni, Matteuzzi, Tommaso, Becattini, Eleonora, Orlandini, Simone, Battista, Francesca, Laiso, Antonio, Nappini, Sergio, Limbucci, Nicola, Renieri, Leonardo, Carangelo, Biagio R., Mangiafico, Salvatore, Della Puppa, Alessandro
• Format: Journal Article
• Language: English
• Published: Vienna Springer Vienna 01.12.2020; Springer Nature B.V
• Subjects: Adult; Aged; Aneurysm; Cerebrospinal Fluid Shunts; Female; Humans; Hydrocephalus; Hydrocephalus - etiology; Hydrocephalus - surgery; Interventional Radiology; Learning algorithms; Machine Learning; Male; Medicine; Medicine & Public Health; Middle Aged; Minimally Invasive Surgery; Morbidity; Neurology; Neuroradiology; Neurosurgery; Original - Vascular Neurosurgery - Other; Original Article - Vascular Neurosurgery - Other; Prognosis; Retrospective Studies; Risk Factors; Risk groups; Stroke; Subarachnoid hemorrhage; Subarachnoid Hemorrhage - complications; Surgical Orthopedics; Vascular Neurosurgery – Other; Shunt-dependency; Hydrocephalus; Subarachnoid hemorrhage; Prognostic models; Machine learning

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 tec...