FairEHR-CLP: Towards Fairness-Aware Clinical Predictions with Contrastive Learning in Multimodal Electronic Health Records

Understand

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 FairEHR-CLP: a general framework for Fairness-aware Clinical Predictions with Contrastive Learning in EHRs.
• FairEHR-CLP operates through a two-stage process, utilizing patient demographics, longitudinal data, and clinical notes.