OBJECT: The authors describe the artificial neural network (ANN) as an innovative and powerful modeling tool that can be increasingly applied to develop predictive models in neurosurgery. They aimed to demonstrate the utility of an ANN in predicting survival following traumatic brain injury and compare its predictive ability with that of regression models and clinicians. METHODS: The authors designed an ANN to predict in-hospital survival following traumatic brain injury. The model was generated with 11 clinical inputs and a single output. Using a subset of the National Trauma Database, the authors "trained" the model to predict outcome by providing the model with patients for whom 11 clinical inputs were paired with known outcomes, which allowed the ANN to "learn" the relevant relationships that predict outcome. The model was tested against actual outcomes in a novel subset of 100 patients derived from the same database. For comparison with traditional forms of modeling, 2 regression models were developed using the same training set and were evaluated on the same testing set. Lastly, the authors used the same 100-patient testing set to evaluate 5 neurosurgery residents and 4 neurosurgery staff physicians on their ability to predict survival on the basis of the same 11 data points that were provided to the ANN. The ANN was compared with the clinicians and the regression models in terms of accuracy, sensitivity, specificity, and discrimination. RESULTS: Compared with regression models, the ANN was more accurate (p < 0.001), more sensitive (p < 0.001), as specific (p = 0.260), and more discriminating (p < 0.001). There was no difference between the neurosurgery residents and staff physicians, and all clinicians were pooled to compare with the 5 best neural networks. The ANNs were more accurate (p < 0.0001), more sensitive (p < 0.0001), as specific (p = 0.743), and more discriminating (p < 0.0001) than the clinicians. CONCLUSIONS: When given the same limited clinical information, the ANN significantly outperformed regression models and clinicians on multiple performance measures. While this paradigm certainly does not adequately reflect a real clinical scenario, this form of modeling could ultimately serve as a useful clinical decision support tool. As the model evolves to include more complex clinical variables, the performance gap over clinicians and logistic regression models will persist or, ideally, further increase.
OBJECT: The authors describe the artificial neural network (ANN) as an innovative and powerful modeling tool that can be increasingly applied to develop predictive models in neurosurgery. They aimed to demonstrate the utility of an ANN in predicting survival following traumatic brain injury and compare its predictive ability with that of regression models and clinicians. METHODS: The authors designed an ANN to predict in-hospital survival following traumatic brain injury. The model was generated with 11 clinical inputs and a single output. Using a subset of the National Trauma Database, the authors "trained" the model to predict outcome by providing the model with patients for whom 11 clinical inputs were paired with known outcomes, which allowed the ANN to "learn" the relevant relationships that predict outcome. The model was tested against actual outcomes in a novel subset of 100 patients derived from the same database. For comparison with traditional forms of modeling, 2 regression models were developed using the same training set and were evaluated on the same testing set. Lastly, the authors used the same 100-patient testing set to evaluate 5 neurosurgery residents and 4 neurosurgery staff physicians on their ability to predict survival on the basis of the same 11 data points that were provided to the ANN. The ANN was compared with the clinicians and the regression models in terms of accuracy, sensitivity, specificity, and discrimination. RESULTS: Compared with regression models, the ANN was more accurate (p < 0.001), more sensitive (p < 0.001), as specific (p = 0.260), and more discriminating (p < 0.001). There was no difference between the neurosurgery residents and staff physicians, and all clinicians were pooled to compare with the 5 best neural networks. The ANNs were more accurate (p < 0.0001), more sensitive (p < 0.0001), as specific (p = 0.743), and more discriminating (p < 0.0001) than the clinicians. CONCLUSIONS: When given the same limited clinical information, the ANN significantly outperformed regression models and clinicians on multiple performance measures. While this paradigm certainly does not adequately reflect a real clinical scenario, this form of modeling could ultimately serve as a useful clinical decision support tool. As the model evolves to include more complex clinical variables, the performance gap over clinicians and logistic regression models will persist or, ideally, further increase.
Authors: Andrew T Hale; David P Stonko; Jaims Lim; Oscar D Guillamondegui; Chevis N Shannon; Mayur B Patel Journal: J Neurosurg Pediatr Date: 2018-11-02 Impact factor: 2.713