Recent research has shown the potential for neural networks to improve upon classical survival models such as the Cox model, which is widely used in clinical practice. Neural networks, however, typically rely on data that are centrally available, whereas healthcare data are frequently held in secure silos. We present a federated Cox model that accommodates this data setting and also relaxes the proportional hazards assumption, allowing time-varying covariate effects. In this latter respect, our model does not require explicit specification of the time-varying effects, reducing upfront organisational costs compared to previous works. We experiment with publicly available clinical datasets and demonstrate that the federated model is able to perform as well as a standard model.
翻译:最近的研究显示,神经网络有可能改进古典生存模型,如临床实践中广泛使用的Cox模型,神经网络通常依赖中央可获取的数据,而保健数据则往往保存在安全的筒仓中。我们提出了一个联盟式的Cox模型,该模型适应了这种数据设置,并放松了相称的危害假设,允许时间相移的共变效应。在这方面,我们的模型并不要求明确具体说明时间变化效应,比以往的工程减少前期组织成本。我们试验了公开的临床数据集,并证明联盟式模型既能够运行,也能运行标准模型。