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 |