[HTML][HTML] Using an artificial neural networks (ANNs) model for prediction of intensive care unit (ICU) outcome and length of stay at hospital in traumatic patients
C Gholipour, F Rahim, A Fakhree… - Journal of clinical and …, 2015 - ncbi.nlm.nih.gov
Journal of clinical and diagnostic research: JCDR, 2015•ncbi.nlm.nih.gov
Materials and Methods We used Neuro-Solution software (NS), a leading-edge neural
network software for data mining to create highly accurate and predictive models using
advanced preprocessing techniques, intelligent automated neural network topology through
cutting-edge distributed computing. This ANN model was used based on back-propagation,
feed forward, and fed by Trauma and injury severity score (TRISS) components, biochemical
findings, risk factors and outcome of 95 patients. In the next step a trained ANN was used to …
network software for data mining to create highly accurate and predictive models using
advanced preprocessing techniques, intelligent automated neural network topology through
cutting-edge distributed computing. This ANN model was used based on back-propagation,
feed forward, and fed by Trauma and injury severity score (TRISS) components, biochemical
findings, risk factors and outcome of 95 patients. In the next step a trained ANN was used to …
Materials and Methods
We used Neuro-Solution software (NS), a leading-edge neural network software for data mining to create highly accurate and predictive models using advanced preprocessing techniques, intelligent automated neural network topology through cutting-edge distributed computing. This ANN model was used based on back-propagation, feed forward, and fed by Trauma and injury severity score (TRISS) components, biochemical findings, risk factors and outcome of 95 patients. In the next step a trained ANN was used to predict outcome, ICU and ward length of stay for 30 test group patients by processing primary data.
Results
The sensitivity and specificity of an ANN for predicting the outcome of traumatic patients in this study calculated 75% and 96.26%, respectively. 93.33% of outcome predictions obtained by ANN were correct. In 3.33% of predictions, results of ANN were optimistic and 3.33% of cases predicted ANN results were worse than the actual outcome of patients. Neither difference in average length of stay in the ward and ICU with predicted ANN results, were statistically significant. Correlation coefficient of two variables of ANN prediction and actual length of stay in hospital was equal to 0.643.
Conclusion
Using ANN model based on clinical and biochemical variables in patients with moderate to severe traumatic injury, resulted in satisfactory outcome prediction when applied to a test set.
ncbi.nlm.nih.gov
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