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

arXiv: https://arxiv.org/abs/2402.00955 · HTML: https://mlsanity.com/paper/2402.00955/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 27 of its 48 references that are in the map.
- First posted on arXiv 1 Feb 2024.

## Builds on (most cited first, 27 of 27)

- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Llama 2: Open Foundation and Fine-Tuned Chat Models](https://mlsanity.com/paper/2307.09288/) (2023)
- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [Supervised Contrastive Learning](https://mlsanity.com/paper/2004.11362/) (2020)
- [Equality of Opportunity in Supervised Learning](https://mlsanity.com/paper/1610.02413/) (2016)
- [Publicly Available Clinical BERT Embeddings](https://mlsanity.com/paper/1904.03323/) (2019)
- [Certifying and removing disparate impact](https://mlsanity.com/paper/1412.3756/) (2014)
- [Mitigating Unwanted Biases with Adversarial Learning](https://mlsanity.com/paper/1801.07593/) (2018)
- [A Survey on Contrastive Self-supervised Learning](https://mlsanity.com/paper/2011.00362/) (2020)
- [Debiased Contrastive Learning](https://mlsanity.com/paper/2007.00224/) (2020)
- [Bias Mitigation Post-processing for Individual and Group Fairness](https://mlsanity.com/paper/1812.06135/) (2018)
- [Fairness via Representation Neutralization](https://mlsanity.com/paper/2106.12674/) (2021)
- [Hyperbolic Contrastive Learning for Visual Representations beyond Objects](https://mlsanity.com/paper/2212.00653/) (2022)
- [Fairness Through Computationally-Bounded Awareness](https://mlsanity.com/paper/1803.03239/) (2018)
- [Generating Synthetic Mixed-type Longitudinal Electronic Health Records for Artificial Intelligent Applications](https://mlsanity.com/paper/2112.12047/) (2021)
- [Fairness-aware Adversarial Perturbation Towards Bias Mitigation for Deployed Deep Models](https://mlsanity.com/paper/2203.01584/) (2022)
- [Contrastive Learning Reduces Hallucination in Conversations](https://mlsanity.com/paper/2212.10400/) (2022)
- [Are Large Language Models Ready for Healthcare? A Comparative Study on Clinical Language Understanding](https://mlsanity.com/paper/2304.05368/) (2023)
- [BERTSurv: BERT-Based Survival Models for Predicting Outcomes of Trauma Patients](https://mlsanity.com/paper/2103.10928/) (2021)
- [Integrating Physiological Time Series and Clinical Notes with Transformer for Early Prediction of Sepsis](https://mlsanity.com/paper/2203.14469/) (2022)
- [SCEHR: Supervised Contrastive Learning for Clinical Risk Prediction using Electronic Health Records](https://mlsanity.com/paper/2110.04943/) (2021)
- [Patchwork Learning: A Paradigm Towards Integrative Analysis across Diverse Biomedical Data Sources](https://mlsanity.com/paper/2305.06217/) (2023)
- [Empirical Quantitative Analysis of COVID-19 Forecasting Models](https://mlsanity.com/paper/2110.00174/) (2021)
- [Contrastive Learning of Temporal Distinctiveness for Survival Analysis in Electronic Health Records](https://mlsanity.com/paper/2308.13104/) (2023)
- [Predicting the Need for Blood Transfusion in Intensive Care Units with Reinforcement Learning](https://mlsanity.com/paper/2206.14198/) (2022)
- [Fair Patient Model: Mitigating Bias in the Patient Representation Learned from the Electronic Health Records](https://mlsanity.com/paper/2306.03179/) (2023)
- [Empirical Analysis of Machine Learning Configurations for Prediction of Multiple Organ Failure in Trauma Patients](https://mlsanity.com/paper/2103.10929/) (2021)

## Built on by (most cited first, 6 of 6)

- [RUPBench: Benchmarking Reasoning Under Perturbations for Robustness Evaluation in Large Language Models](https://mlsanity.com/paper/2406.11020/) (2024)
- [Equitable Electronic Health Record Prediction with FAME: Fairness-Aware Multimodal Embedding](https://mlsanity.com/paper/2506.13104/) (2025)
- [Critical Appraisal of Fairness Metrics in Clinical Predictive AI](https://mlsanity.com/paper/2506.17035/) (2025)
- [MultiFair: Multimodal Balanced Fairness-Aware Medical Classification with Dual-Level Gradient Modulation](https://mlsanity.com/paper/2510.07328/) (2025)
- [Amplifying Prominent Representations in Multimodal Learning via Variational Dirichlet Process](https://mlsanity.com/paper/2510.20736/) (2025)
- [Cross-Modal Alignment via Variational Copula Modelling](https://mlsanity.com/paper/2511.03196/) (2025)

## Abstract (arXiv)

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.