title: Neural networks for ischemic stroke.
abstract: BACKGROUND
  To have uniform criteria for evaluating populations for prevalence of transient ischemic attack (TIA)/stroke, validated instruments are necessary for objective assessment and classification.
  
  
  METHODS
  Patient responses compatible with symptoms of TIA or ischemic stroke, obtained from participants in a substudy of the Asymptomatic Carotid Atherosclerosis Study, were used to program a neural network for each symptom. Models were designed for rapid classification into 1 of 7 outputs: no event, TIA, or stroke (in left carotid, right carotid, or vertebrobasilar). The networks were then tested by comparing decisions with a validated questionnaire used to access an independent data set of 381 patients.
  
  
  RESULTS
  There were 144 patients who reported sudden speech change, 89 with sudden vision loss, 67 with double vision, 189 with sudden numbness, 223 with episodic dizziness, and 108 with paralysis, for a total of 820 reported symptoms among the 381 patients tested. For each category, an equal number of individuals reporting "No" to these phenomena were randomly selected and analyzed. Neural network classification correlated with the diagnoses made by specially trained stroke clinicians (e.g., all who responded "No" were correctly classified as having no neurologic event). Ten symptomatic patients were misclassified, with the most common reason being incomplete data. After adjustment of the network logic, these misclassifications did not recur.
  
  
  CONCLUSIONS
  Computer networks can be trained to produce a rapid and accurate classification of TIA or stroke by vascular distribution, enabling screening of populations for assessment of their incidence and prevalence.
authors: Department of Neurology, Wake Forest University School of Medicine, Winston-Salem, North Carolina, USA.
authors: Ralph W
authors: RW
authors: Barnes
authors: James F
authors: JF
authors: Toole
authors: J J
authors: JJ
authors: Nelson
authors: Virginia J
authors: VJ
authors: Howard