2024-11-25-gao24a.md

title	abstract	openreview	layout	series	publisher	issn	id	month	tex_title	cycles	bibtex_author	author	date	address	container-title	volume	genre	issued	pdf	extras
Predicting Long-Term Allograft Survival in Liver Transplant Recipients	Liver allograft failure occurs in approximately 20% of liver transplant recipients within five years post-transplant, leading to mortality or the need for retransplantation. Providing an accurate and interpretable model for individualized risk estimation of graft failure is essential for improving post-transplant care. To this end, we introduce the Model for Allograft Survival (MAS), a simple linear risk score that outperforms other advanced survival models. Using longitudinal patient follow-up data from the United States (U.S.), we develop our models on 82,959 liver transplant recipients and conduct multi-site evaluations on 11 regions. Additionally, by testing on a separate non-U.S. cohort, we explore the out-of-distribution generalization performance of various models without additional fine-tuning, a crucial property for clinical deployment. We find that the most complex models are also the ones most vulnerable to distribution shifts despite achieving the best in-distribution performance. Our findings not only provide a strong risk score for predicting long-term graft failure but also suggest that the routine machine learning pipeline with only in-distribution held-out validation could create harmful consequences for patients at deployment.	JhvatSLKhG	inproceedings	Proceedings of Machine Learning Research	PMLR	2640-3498	gao24a	0	Predicting Long-Term Allograft Survival in Liver Transplant Recipients	false	Gao, Xiang and Cooper, Michael and Naghibzadeh, Maryam and Azhie, Amirhossein and Bhat, Mamatha and Krishnan, Rahul	given family Xiang Gao given family Michael Cooper given family Maryam Naghibzadeh given family Amirhossein Azhie given family Mamatha Bhat given family Rahul Krishnan	2024-11-25		Proceedings of the 9th Machine Learning for Healthcare Conference	252	inproceedings	date-parts 2024 11 25	https://raw.githubusercontent.com/mlresearch/v252/main/assets/gao24a/gao24a.pdf