Survival Prediction Using Deep Learning
Proceedings of AAAI Spring Symposium on Survival Prediction - Algorithms, Challenges, and Applications 2021, PMLR 146:207-214, 2021.
In many biomedical applications, outcome is measured as a “time-to-event” (e.g., time-to-disease progression or death). Cox proportional hazards (CoxPH) model has been widely used to assess the association between baseline characteristics of a patient and this outcome. Meanwhile, in therapeutic areas such as Oncology, clinical imaging (e.g. computerized tomography (CT) scan) is widely used for detection, diagnosis of disease, monitoring of progression and treatment effect. We are interested in using such images with neural network to build predictive models with survival data. However, the standard methodologies cannot be applied to imaging data with time-to-event outcome due to challenges such as memory constraint. In this work, we develop a simple methodology to engage images with survival data. Our proposed methodology is a modified version of CoxPH model that is amenable to SGD and allows us to overcome the existing challenges. We present the neural network architecture for the survival prediction using images. Our architecture can leverage new advances in network topology.