  Electronic Health Records (EHRs) contain rich patient information and are
crucial for clinical research and practice. In recent years, deep learning
models have been applied to EHRs, but they often rely on massive features,
which may not be readily available for all patients. We propose HTP-Star, which
leverages hypergraph structures with a pretrain-then-finetune framework for
modeling EHR data, enabling seamless integration of additional features.
Additionally, we design two techniques, namely (1) Smoothness-inducing
Regularization and (2) Group-balanced Reweighting, to enhance the model's
robustness during fine-tuning. Through experiments conducted on two real EHR
datasets, we demonstrate that HTP-Star consistently outperforms various
baselines while striking a balance between patients with basic and extra
features.
