Summary of Fairehr-clp: Towards Fairness-aware Clinical Predictions with Contrastive Learning in Multimodal Electronic Health Records, by Yuqing Wang et al.
FairEHR-CLP: Towards Fairness-Aware Clinical Predictions with Contrastive Learning in Multimodal Electronic Health Records
by Yuqing Wang, Malvika Pillai, Yun Zhao, Catherine Curtin, Tina Hernandez-Boussard
First submitted to arxiv on: 1 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computers and Society (cs.CY)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary In this paper, researchers develop a new framework called FairEHR-CLP to ensure fairness in predictive models for healthcare. The goal is to mitigate social biases that can be embedded in electronic health records (EHRs). The approach involves generating synthetic patient data and then using contrastive learning to align patient representations across different demographic groups. This process is designed to preserve essential health information while reducing biased predictions. The framework also includes a novel fairness metric to measure error rate disparities across subgroups. Experimental results on three EHR datasets show that FairEHR-CLP outperforms competitive baselines in terms of both fairness and utility. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary FairEHR-CLP is a new way to make sure medical predictions are fair. Right now, some medical models can be biased because they’re based on data that’s not very diverse. This framework tries to fix that by creating fake patient data that’s more diverse and then using special learning techniques to make the model less biased. The goal is to make sure the model is accurate for everyone, no matter their background or health information. |
