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Self-supervised Representation Learning on Electronic Health Records with Graph Kernel Infomax
We propose Graph Kernel Infomax, a self-supervised graph kernel learning approach on the graphical representation of EHR, to overcome the previous problems.
The Analysis from Nonlinear Distance Metric to Kernel-based Drug Prescription Prediction System
Distance metrics and their nonlinear variant play a crucial role in machine learning based real-world problem solving.
Cross-Global Attention Graph Kernel Network Prediction of Drug Prescription
We present an end-to-end, interpretable, deep-learning architecture to learn a graph kernel that predicts the outcome of chronic disease drug prescription.
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