title: Neural network modeling accurately predicts the functional outcome of stroke survivors with moderate disabilities.
abstract: OBJECTIVE
  To predict the place of discharge or discharge Functional Independence Measure (FIM) score for stroke survivors with moderate disability using neural network modeling. Our previous work demonstrated that the FIM predicts the level of recovery for stroke survivors with either severe or mild disabilities.
  
  
  METHODS
  Neural network analysis.
  
  
  METHODS
  Tertiary care rehabilitation program.
  
  
  METHODS
  One hundred forty-seven consecutive stroke survivors admitted for rehabilitation with admission FIM scores between 37 and 96 were used as the training and internal test set. Seventeen other randomly selected stroke survivors were used as the external test set.
  
  
  METHODS
  A neural network model was developed using a small set of clinical variables and the admission FIM score.
  
  
  METHODS
  Neural network model predicting place of discharge or discharge FIM score.
  
  
  RESULTS
  A working and accurate model was developed to predict the discharge FIM score. The model was able to predict the 17 external test cases with an accuracy = 88%, sensitivity = 83%, specificity = 91%, positive predictive value = 83%, and negative predictive value = 91%.
  
  
  CONCLUSIONS
  Neural network modeling is useful in the prediction of functional recovery and helps in discharge planning and allocation of rehabilitation resources.
authors.affiliation: Hamilton Civic Hospitals, McMaster University, Ontario, Canada.
authors.forename: W J
authors.initials: WJ
authors.lastname: Oczkowski
authors.forename: S
authors.initials: S
authors.lastname: Barreca
mesh_terms: D000368:Aged; D002561:Cerebrovascular Disorders / Q000534:rehabilitation*; D005260:Female; D006801:Humans; D008297:Male; D016571:Neural Networks, Computer; D011379:Prognosis; D012680:Sensitivity and Specificity; D017741:Survivors; D016896:Treatment Outcome