FAME通过公平性加权多模态嵌入，让医疗预测更公平
Equitable Electronic Health Record Prediction with FAME: Fairness-Aware Multimodal Embedding
- 按各模态对公平性的贡献动态加权，平衡不同群体表现
- 在多个任务上同时提升性能与公平性，优于现有基线
- 适合关注医疗AI公平性的研究者与临床应用开发者
电子健康记录（EHR）包含文本、图像和医疗编码等多种模态数据，对临床决策至关重要。为处理这些复杂数据，多模态AI（MAI）已成为融合信息的强大方法。然而，大多数现有MAI模型仅优化预测性能，可能加剧患者亚群间的偏见。尽管已有减少偏见的技术，但各模态的独立优势及其在降低偏见与优化性能中的协同作用仍缺乏探索。本文提出FAME（Fairness-Aware Multimodal Embeddings），一种根据各模态公平性贡献显式加权的框架。FAME通过联合损失函数优化性能与公平性，并利用误差分布差异指数（EDDI）衡量子群体间公平性，提出无符号聚合方法以均衡各群体表现，确保模型结果公平。我们在BEHRT与BioClinicalBERT基础上，结合结构化与非结构化EHR数据，在多个预测任务中评估FAME，结果表明其在性能与公平性方面均优于其他基线。
原文摘要 · Abstract (English)
Electronic Health Record (EHR) data encompass diverse modalities -- text, images, and medical codes -- that are vital for clinical decision-making. To process these complex data, multimodal AI (MAI) has emerged as a powerful approach for fusing such information. However, most existing MAI models optimize for better prediction performance, potentially reinforcing biases across patient subgroups. Although bias-reduction techniques for multimodal models have been proposed, the individual strengths of each modality and their interplay in both reducing bias and optimizing performance remain underexplored. In this work, we introduce FAME (Fairness-Aware Multimodal Embeddings), a framework that explicitly weights each modality according to its fairness contribution. FAME optimizes both performance and fairness by incorporating a combined loss function. We leverage the Error Distribution Disparity Index (EDDI) to measure fairness across subgroups and propose a sign-agnostic aggregation method to balance fairness across subgroups, ensuring equitable model outcomes. We evaluate FAME with BEHRT and BioClinicalBERT, combining structured and unstructured EHR data, and demonstrate its effectiveness in terms of performance and fairness compared with other baselines across multiple EHR prediction tasks.
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