Cancer Treatment Optimization Using Gaussian Processes

In this work, we present a specific case study where we aim to optimize personalized phar- macological treatment strategies for cancer. We tackle this problem under the contextual bandit setting using Gaussian processes (GPs) to model the reward function associated with each treatment over the set of contexts, which correspond to tumour sizes. We experiment with different GP configurations to study the robustness of the recommended strategies with regard to the modelling. Our results show that the rec- ommendations seem robust to the GP configuration and allow us to identify future work to improve our recommendations considering the constraints and challenges of this specific application.

Authors

• Audrey Durand Université Laval Canada CIFAR AI Chair
• Joelle Pineau

Keywords

No keywords are indexed for this paper.