Uncertainty Estimation in Cancer Survival Prediction
Published in ICLR Workshop on AI for Affordable Heathcare, 2020
Survival models are used in various fields, such as the development of cancer treatment protocols. Although many statistical and machine learning models have been proposed to achieve accurate survival predictions, little attention has been paid to obtain well-calibrated uncertainty estimates associated with each prediction. The currently popular models are opaque and untrustworthy in that they often express high confidence even on those test cases that are not similar to the training samples and even if their predictions are wrong. We propose a Bayesian framework for survival models that not only gives more accurate survival predictions but also quantifies the survival uncertainty better. Our approach is a novel combination of variational inference for uncertainty estimation, neural multi-task logistic regression for estimating nonlinear and time-varying risk models, and an additional sparsity-inducing prior to work with high dimensional data.
Citation
‘Hrushikesh Loya, Pranav Poduval, Deepak Anand, Neeraj Kumar, Amit Sethi (2020). "Uncertainty Estimation in Cancer Survival Prediction; 2020 Apr 26-30; Addis Ababa, Ethiopia</i>.’