A Bayesian framework to quantify survival uncertainty
Published in ESMO MAP, 2019
This paper provides a Bayesian framework for quantifying survival uncertainity using deep esembles. The input to network is PAM50 gene set and clinical variables. Several methods like Cox-Propotional Hazard Model, Multi-Task Linear Reression and their Bayesian extension are compared.
Citation
‘Hrushikesh Loya, Anand D, Kumar N, Sethi A. (2019). "A Bayesian framework to quantify survival uncertainty." Annals of Oncology 2019, Volume 30, Issue Supplement_7, November 2019.’