Multiple Graph Kernel Fusion Prediction of Drug Prescription

Abstract

We present an end-to-end interpretable deep architecture that predicts the success of drug prescription based on multiple graph kernel fusion using a graphical representation of electronic health records. We formulate the predictive model as a binary graph classification problem with a set of graph kernels proposed to capture different aspects of graph structures through deep neural networks. Results using the Taiwanese National Health Insurance Research Database demonstrate that our approach outperforms current start-of-the-art models on accuracy and interpretability. The approach is in preliminary deployment.