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Survival prediction and staging on MM EHR

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dc.contributor.author Das, Soham
dc.contributor.author Gupta, Anubha (Advisor)
dc.date.accessioned 2023-04-15T10:02:10Z
dc.date.available 2023-04-15T10:02:10Z
dc.date.issued 2022-05
dc.identifier.uri http://repository.iiitd.edu.in/xmlui/handle/123456789/1172
dc.description.abstract AI methods for survival analysis and risk staging has been a prevalent topic of research, and many models for both tasks have been introduced into the healthcare industry. Many deep learning methods for calculating hazards, predicting overall survival times and survival probability prediction have been developed, as well as many rule based risk staging schemes. The aim of this research work is to create an architecture that can conduct both tasks at once, and form a connection between survival curve of a patient and their risk group. We propose a novel methodology for the same, and report statistically significant results. en_US
dc.language.iso en_US en_US
dc.publisher IIIT-Delhi en_US
dc.subject clustering en_US
dc.subject deep learning en_US
dc.subject survival prediction en_US
dc.subject risk staging en_US
dc.subject cancer en_US
dc.title Survival prediction and staging on MM EHR en_US


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