Division of Neurosurgery, University of Vermont, Burlington, Vermont, USA.
J Neurosurg. 2010 Sep;113(3):585-90. doi: 10.3171/2009.11.JNS09857.
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.
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.
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.
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.
作者将人工神经网络（ANN）描述为一种创新且强大的建模工具，可越来越多地应用于开发神经外科中的预测模型。他们旨在展示 ANN 在预测创伤性脑损伤后生存方面的效用，并将其预测能力与回归模型和临床医生进行比较。
作者设计了一个 ANN 来预测创伤性脑损伤后的院内生存率。该模型使用 11 个临床输入和一个单一输出进行生成。使用国家创伤数据库的一个子集，作者通过向模型提供 11 个临床输入与已知结果配对的患者，使模型“训练”以预测结果，这使得 ANN 可以“学习”预测结果的相关关系。然后，将模型应用于来自同一数据库的 100 名新患者的新子集中的实际结果进行测试。为了与传统建模形式进行比较，使用相同的训练集开发了 2 个回归模型，并在相同的测试集上对它们进行了评估。最后，作者使用相同的 100 名患者测试集来评估 5 名神经外科住院医师和 4 名神经外科工作人员医生根据提供给 ANN 的相同 11 个数据点预测生存的能力。ANN 在准确性、敏感性、特异性和判别力方面与临床医生和回归模型进行了比较。
与回归模型相比，ANN 更准确（p<0.001）、更敏感（p<0.001）、特异性相同（p=0.260）、判别力更高（p<0.001）。神经外科住院医师和工作人员医生之间没有差异，所有临床医生都被汇集在一起与 5 个最佳神经网络进行比较。ANN 在准确性（p<0.0001）、敏感性（p<0.0001）、特异性（p=0.743）和判别力（p<0.0001）方面均优于临床医生。
当提供相同的有限临床信息时，ANN 在多个性能指标上明显优于回归模型和临床医生。虽然这种范例当然不能充分反映真实的临床情况，但这种建模形式最终可能成为一种有用的临床决策支持工具。随着模型的发展包括更复杂的临床变量，临床医生和逻辑回归模型的性能差距将持续存在，或者理想情况下，进一步扩大。
