Response to cancer therapeutics is heterogenous making it hard predict despite advances in machine learning approaches. Here, the authors develop a graph neural network-based approach, Graph-Encoded Mixture Survival (GEMS), to identify ‘predictive subphenotypes’ of patients with similar baseline characteristics and survival outcomes of cancer patients using electronic health records to predict patient response to therapy.
- Weishen Pan
- Deep Hathi
- Fei Wang
