Objective: To characterize social networks in stroke survivors by comparing two established
social network scales to a newly developed personal network mapping tool. Background …

Objective: To investigate the inter-rater reliability between a machine learning Natural
Language Processing (NLP) algorithm and trained data reviewers at identifying acute …

Introduction: Prehospital stroke data is often not included in hospital records and quality
improvement registries. However, manual data abstraction from free-text emergency medical …

Brain magnetic resonance imaging (MRI) is useful for predicting the outcome of patients with
acute ischemic stroke (AIS). Although deep learning (DL) using brain MRI with certain image …

Background Intracerebral hemorrhage (ICH) is a devastating stroke type that causes high
mortality rates and severe disability among survivors. Many prognostic models are available …

Background Manual coding of phenotypes in brain radiology reports is time consuming. We
developed a natural language processing (NLP) algorithm to enable automatic identification …

Objective: The objective of this study was to determine factors associated with disease-
related stigma after hemorrhagic stroke. Background: Stroke survivors may experience …

Objective: To determine factors associated with depression after hemorrhagic stroke.
Background: Depression may affect recovery and functional outcome after hemorrhagic …

According to the World Stroke Organization, 12.2 million people worldwide will have their
first stroke this year almost half of which will die as a result. Natural Language Processing …

The underlying structure of National Institutes of Health Stroke Scale (NIHSS) as the most
widely used scale in clinical trials has been the focus of little attention. The aim of the current …