Northwestern University, USA.
Health Informatics J. 2019 Sep;25(3):878-891. doi: 10.1177/1460458217720395. Epub 2017 Sep 19.
We utilize deep neural networks to develop prediction models for patient survival and conditional survival of colon cancer. Our models are trained and validated on data obtained from the Surveillance, Epidemiology, and End Results Program. We provide an online outcome calculator for 1, 2, and 5 years survival periods. We experimented with multiple neural network structures and found that a network with five hidden layers produces the best results for these data. Moreover, the online outcome calculator provides conditional survival of 1, 2, and 5 years after surviving the mentioned survival periods. In this article, we report an approximate 0.87 area under the receiver operating characteristic curve measurements, higher than the 0.85 reported by Stojadinovic et al.
我们利用深度神经网络为结肠癌患者的生存和条件生存开发预测模型。我们的模型是在 Surveillance、Epidemiology、and End Results Program 获得的数据上进行训练和验证的。我们提供了一个用于 1、2 和 5 年生存期的在线结果计算器。我们尝试了多种神经网络结构,发现具有五个隐藏层的网络对这些数据产生了最佳结果。此外,在线结果计算器提供了在所述生存期后存活 1、2 和 5 年的条件生存。在本文中,我们报告了大约 0.87 的接收器操作特性曲线测量面积,高于 Stojadinovic 等人报告的 0.85。