Self-supervised Representation Learning on Electronic Health Records with Graph Kernel Infomax
arXiv (Cornell University)(2022)
摘要
Learning Electronic Health Records (EHRs) representation is a preeminent yet
under-discovered research topic. It benefits various clinical decision support
applications, e.g., medication outcome prediction or patient similarity search.
Current approaches focus on task-specific label supervision on vectorized
sequential EHR, which is not applicable to large-scale unsupervised scenarios.
Recently, contrastive learning shows great success on self-supervised
representation learning problems. However, complex temporality often degrades
the performance. We propose Graph Kernel Infomax, a self-supervised graph
kernel learning approach on the graphical representation of EHR, to overcome
the previous problems. Unlike the state-of-the-art, we do not change the graph
structure to construct augmented views. Instead, we use Kernel Subspace
Augmentation to embed nodes into two geometrically different manifold views.
The entire framework is trained by contrasting nodes and graph representations
on those two manifold views through the commonly used contrastive objectives.
Empirically, using publicly available benchmark EHR datasets, our approach
yields performance on clinical downstream tasks that exceeds the
state-of-the-art. Theoretically, the variation on distance metrics naturally
creates different views as data augmentation without changing graph structures.
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关键词
electronic health records
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