A Federated Cox Model with Non-Proportional Hazards

Autor:	Zhang, Dekai, Toni, Francesca, Williams, Matthew
Rok vydání:	2022
Předmět:	Computer Science - Machine Learning Statistics - Machine Learning
Druh dokumentu:	Working Paper
Popis:	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. Comment: Accepted for publication in Multimodal AI in Healthcare: A Paradigm Shift in Health Intelligence as part of the book series Studies in Computational Intelligence by Springer
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2207.05050 Zobrazit plný text záznamu View this record from Arxiv