Population / patient phenotype similarity based GCN for survival analysis

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DC Field	Value	Language
dc.contributor.author	Kirtani, Chhavi	-
dc.contributor.author	Prasad, Ranjitha (Advisor)	-
dc.date.accessioned	2023-04-20T09:56:44Z	-
dc.date.available	2023-04-20T09:56:44Z	-
dc.date.issued	2020-12	-
dc.identifier.uri	http://repository.iiitd.edu.in/xmlui/handle/123456789/1226	-
dc.description.abstract	Survival Analysis is an important field of research and has its application in medical fields. Researchers have been experimenting with multiple methods to provide a better predicting model for survival analysis, yet there are many improvements possible. We propose here a method combining two concepts together, i.e., Graph and Non-linear survival models in order to provide a better model for Survival Analysis.	en_US
dc.language.iso	en_US	en_US
dc.publisher	IIIT-Delhi	en_US
dc.subject	Graph	en_US
dc.subject	Risk Function	en_US
dc.subject	Survival Analysis	en_US
dc.subject	Neural Networks	en_US
dc.title	Population / patient phenotype similarity based GCN for survival analysis	en_US
Appears in Collections:	Year-2020

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