DAGSurv: Directed Acyclic Graph Based Survival Analysis Using Deep Neural Networks

Autor:	Sharma, Ansh Kumar, Kukreja, Rahul, Prasad, Ranjitha, Rao, Shilpa
Rok vydání:	2021
Předmět:	Computer Science - Machine Learning Statistics - Machine Learning
Druh dokumentu:	Working Paper
Popis:	Causal structures for observational survival data provide crucial information regarding the relationships between covariates and time-to-event. We derive motivation from the information theoretic source coding argument, and show that incorporating the knowledge of the directed acyclic graph (DAG) can be beneficial if suitable source encoders are employed. As a possible source encoder in this context, we derive a variational inference based conditional variational autoencoder for causal structured survival prediction, which we refer to as DAGSurv. We illustrate the performance of DAGSurv on low and high-dimensional synthetic datasets, and real-world datasets such as METABRIC and GBSG. We demonstrate that the proposed method outperforms other survival analysis baselines such as Cox Proportional Hazards, DeepSurv and Deephit, which are oblivious to the underlying causal relationship between data entities. Comment: 16 pages, Accepted at ACML 2021
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2111.01482 Zobrazit plný text záznamu View this record from Arxiv