2024-07-24-xu24a.md

title	abstract	year	volume	publisher	series	software	layout	issn	id	month	tex_title	firstpage	lastpage	page	order	cycles	bibtex_author	author	date	address	container-title	genre	issued	pdf	extras
From Basic to Extra Features: Hypergraph Transformer Pretrain-then-Finetuning for Balanced Clinical Predictions on EHR	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 \ours{}\footnote{Short for \textbf{H}ypergraph \textbf{T}ransformer \textbf{P}retrain-then-Finetuning with \textbf{S}moo\textbf{t}hness-induced regularization \textbf{a}nd \textbf{R}eweighting.}, 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) \emph{Smoothness-inducing Regularization} and (2) \emph{Group-balanced Reweighting}, to enhance the model’s robustness during finetuning. Through experiments conducted on two real EHR datasets, we demonstrate that \ours{} consistently outperforms va	2024	248	PMLR	Proceedings of Machine Learning Research	https://github.com/ritaranx/HTP-Star	inproceedings	2640-3498	xu24a	0	From Basic to Extra Features: Hypergraph Transformer Pretrain-then-Finetuning for Balanced Clinical Predictions on EHR	182	197	182-197	182	false	Xu, Ran and Lu, Yiwen and Liu, Chang and Chen, Yong and Sun, Yan and Hu, Xiao and Ho, Joyce C and Yang, Carl	given family Ran Xu given family Yiwen Lu given family Chang Liu given family Yong Chen given family Yan Sun given family Xiao Hu given family Joyce C Ho given family Carl Yang	2024-07-24		Proceedings of the fifth Conference on Health, Inference, and Learning	inproceedings	date-parts 2024 7 24	https://raw.githubusercontent.com/mlresearch/v248/main/assets/xu24a/xu24a.pdf