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A leave-one-domain-out (LODO) transfer learning protocol for edge classification on DyTAGs is formally established, and it is demonstrated that state-of-the-art self-supervised methods for dynamic graph learning perform poorly when transferred to unseen domains.
It is shown that stochastic Patient Sampling with controllable weighting improves performance on real-world EHR data, and training and validation sequence construction are identified as important and underexplored design choices for autoregressive EHR foundation models.
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