论文标题
COX-NNET v2.0：改进的基于神经网络的生存预测扩展到大规模EMR数据集
Cox-nnet v2.0: improved neural-network based survival prediction extended to large-scale EMR dataset
论文作者
论文摘要
COX-NNET是一种基于神经网络的预后预测方法，最初应用于基因组学数据。在这里，我们提出了Cox-NNET的2版，对效率和可解释性有了显着提高，因此可以根据大规模电子病历（EMR）数据集预测预后。我们还添加了基于置换的特征重要性得分和特征系数的方向。 Cox-NNET v2.0应用于OPTN肾脏移植的EMR数据集，将COX-NNET的训练时间降低了32倍（n = 10,000），并且比Cox-ph获得了更好的预测准确性（p <0.05）。可用性和实施：COX-NNET v2.0可以在https://github.com/lanagarmire/cox-nnet-v2.0上免费提供。
Cox-nnet is a neural-network based prognosis prediction method, originally applied to genomics data. Here we propose the version 2 of Cox-nnet, with significant improvement on efficiency and interpretability, making it suitable to predict prognosis based on large-scale electronic medical records (EMR) datasets. We also add permutation-based feature importance scores and the direction of feature coefficients. Applying on an EMR dataset of OPTN kidney transplantation, Cox-nnet v2.0 reduces the training time of Cox-nnet up to 32 folds (n=10,000) and achieves better prediction accuracy than Cox-PH (p<0.05). Availability and implementation: Cox-nnet v2.0 is freely available to the public at https://github.com/lanagarmire/Cox-nnet-v2.0
