2024-11-25-wang24a.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
FairEHR-CLP: Towards Fairness-Aware Clinical Predictions with Contrastive Learning in Multimodal Electronic Health Records	In the high-stakes realm of healthcare, ensuring fairness in predictive models is crucial. Electronic Health Records (EHRs) have become integral to medical decision-making, yet existing methods for enhancing model fairness restrict themselves to unimodal data and fail to address the multifaceted social biases intertwined with demographic factors in EHRs. To mitigate these biases, we present $\textit{FairEHR-CLP}$: a general framework for $\textbf{Fair}$ness-aware Clinical $\textbf{P}$redictions with $\textbf{C}$ontrastive $\textbf{L}$earning in $\textbf{EHR}$s. FairEHR-CLP operates through a two-stage process, utilizing patient demographics, longitudinal data, and clinical notes. First, synthetic counterparts are generated for each patient, allowing for diverse demographic identities while preserving essential health information. Second, fairness-aware predictions employ contrastive learning to align patient representations across sensitive attributes, jointly optimized with an MLP classifier with a softmax layer for clinical classification tasks. Acknowledging the unique challenges in EHRs, such as varying group sizes and class imbalance, we introduce a novel fairness metric to effectively measure error rate disparities across subgroups. Extensive experiments on three diverse EHR datasets on three tasks demonstrate the effectiveness of FairEHR-CLP in terms of fairness and utility compared with competitive baselines. FairEHR-CLP represents an advancement towards ensuring both accuracy and equity in predictive healthcare models.	DpRiqEgzEM	inproceedings	Proceedings of Machine Learning Research	PMLR	2640-3498	wang24a	0	Fair{EHR}-{CLP}: Towards Fairness-Aware Clinical Predictions with Contrastive Learning in Multimodal Electronic Health Records	false	Wang, Yuqing and Pillai, Malvika and Zhao, Yun and Curtin, Catherine M and Hernandez-Boussard, Tina	given family Yuqing Wang given family Malvika Pillai given family Yun Zhao given family Catherine M Curtin given family Tina Hernandez-Boussard	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/wang24a/wang24a.pdf