Spatial neglect in the digital age: Influence of presentation format on patients' test behavior.
Hannah RosenzopfChristoph SperberFranz WorthaDaniel WiesenAnnika MuthElise KleinKorbinian MöllerHans-Otto KarnathPublished in: Journal of the International Neuropsychological Society : JINS (2022)
The CoC seems robust to both test digitization and display size adaptations. Machine learning classification based on the additional variables derived from computerized tests succeeded in distinguishing stroke patients with spatial neglect from those without. The investigated additional variables have the potential to aid in neglect diagnosis, in particular when the CoC cannot be validly assessed (e.g., when the test is not performed to completion).
Keyphrases
- machine learning
- end stage renal disease
- newly diagnosed
- ejection fraction
- chronic kidney disease
- deep learning
- atrial fibrillation
- prognostic factors
- artificial intelligence
- peritoneal dialysis
- big data
- high intensity
- mass spectrometry
- atomic force microscopy
- risk assessment
- cerebral ischemia
- subarachnoid hemorrhage