论文标题
癌症存活预测的不确定性估计
Uncertainty Estimation in Cancer Survival Prediction
论文作者
论文摘要
生存模型用于各个领域，例如癌症治疗方案的发展。尽管已经提出了许多统计和机器学习模型来实现准确的生存预测，但很少有人注意以获得与每个预测相关的良好校准的不确定性估计值。当前受欢迎的模型是不透明和不信任的，因为即使在那些与训练样本不同的测试用例，即使他们的预测是错误的，它们也经常表现出很高的信心。我们为生存模型提出了一个贝叶斯框架，该框架不仅提供了更准确的生存预测，而且可以更好地量化生存不确定性。我们的方法是对不确定性估计的变异推断，神经多任务逻辑回归的新型组合，用于估计非线性和时变风险模型，以及在使用高维数据之前引起额外的稀疏性诱导。
Survival models are used in various fields, such as the development of cancer treatment protocols. Although many statistical and machine learning models have been proposed to achieve accurate survival predictions, little attention has been paid to obtain well-calibrated uncertainty estimates associated with each prediction. The currently popular models are opaque and untrustworthy in that they often express high confidence even on those test cases that are not similar to the training samples, and even when their predictions are wrong. We propose a Bayesian framework for survival models that not only gives more accurate survival predictions but also quantifies the survival uncertainty better. Our approach is a novel combination of variational inference for uncertainty estimation, neural multi-task logistic regression for estimating nonlinear and time-varying risk models, and an additional sparsity-inducing prior to work with high dimensional data.
