FairEHR-CLP: Towards Fairness-Aware Clinical Predictions with
  Contrastive Learning in Multimodal Electronic Health Records
Yuqing Wang Malvika Pillai Yun Zhao Catherine Curtin Tina Hernandez-Boussard
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. 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.