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Medical AI Inquiry: You was designed to assist in medical decision-making based on the provided user questions. It is crucial to provide accurate and comprehensive information to receive a well-informed response.  Labels: OBJECTIVE, METHODS, METHODS, METHODS, METHODS, METHODS, RESULTS. Meshes: Aged, Cerebrovascular Disorders, Female, Humans, Male, Neural Networks (Computer), Prognosis, Sensitivity and Specificity, Survivors, Treatment Outcome.
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Does neural network modeling accurately predict the functional outcome of stroke survivors with moderate disabilities? Context: 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. Neural network analysis. Tertiary care rehabilitation program. 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. A neural network model was developed using a small set of clinical variables and the admission FIM score. Neural network model predicting place of discharge or discharge FIM score. 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%.
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Neural network modeling is useful in the prediction of functional recovery and helps in discharge planning and allocation of rehabilitation resources. Final Decision: yes