Purpose This study aims to develop a machine learning-based model for predicting mortality
risk in patients with epilepsy admitted to the intensive care unit (ICU), providing clinicians …

Introduction Current electroencephalography (EEG) practice relies on interpretation by
expert neurologists, which introduces diagnostic and therapeutic delays that can impact …

Objective To determine whether machine learning techniques would enhance our ability to
incorporate key variables into a parsimonious model with optimized prediction performance …

Objective This study was undertaken to validate the accuracy of the Epidemiology‐Based
Mortality Score in Status Epilepticus (EMSE) in predicting the risk of death at 30 days in a …

Objective. Epileptic seizures are relatively common in critically-ill children admitted to the
pediatric intensive care unit (PICU) and thus serve as an important target for identification …

Background Research on patients with cerebral infarction in the Intensive Care Unit (ICU) is
still lacking. Our study aims to develop and validate multiple machine-learning (ML) models …

Good to excellent predictive models are now available to guide medical and surgical
epilepsy decision-making with nomograms offering individualized predictions and user …

Objective To use clinically informed machine learning to derive prediction models for early
and late premature death in epilepsy. Methods This was a population‐based primary care …

Background Better outcome prediction could assist in reliable classification of the illnesses
in neurological intensive care unit (ICU) severity to support clinical decision-making. We …

This article introduces a machine learning approach, based on a nonparametric, decision-
tree-based random survival forest (RSF) model, for a continuous prognosis of epileptic …