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.

我们提出了 HTP-Star，它利用超图结构和预训练再微调的框架来模拟电子健康记录数据，实现了额外特征的无缝集成，并设计了两种技术来增强模型的鲁棒性。通过在两个真实电子健康记录数据集上进行实验，我们证明了 HTP-Star 在基础特征和额外特征的病人之间取得了平衡，并一直优于各种基准模型。