FairMed-XGB: 一种具有可解释性的多指标框架，用于关键医疗数据中的公平性
FairMed-XGB: A Bayesian-Optimised Multi-Metric Framework with Explainability for Demographic Equity in Critical Healthcare Data
AI总结：
本文提出FairMed-XGB框架，通过整合统计平衡差、Theil指数和Wasserstein距离的公平性感知损失函数，结合贝叶斯搜索优化XGBoost分类器，有效减少性别偏差并保持模型性能和透明度。
摘要：
在关键护理环境中部署的机器学习模型表现出人口统计学偏见，特别是性别差异，这会削弱临床信任和公平治疗。本文介绍了一种新的框架FairMed-XGB，系统地检测并减轻基于性别的预测偏差，同时保持模型性能和透明度。该框架整合了一个公平性感知损失函数，结合统计平衡差、Theil指数和Wasserstein距离，并通过贝叶斯搜索优化到XGBoost分类器中。在七个临床不同的队列上进行后缓解评估，来自MIMIC-IV-ED和eICU数据库的评估显示显著的偏见减少：统计平衡差在MIMIC-IV-ED上减少40至51%，在eICU上减少10至19%；Theil指数减少四到五个数量级至接近零；Wasserstein距离减少20至72%。这些收益是在预测准确性（AUC-ROC下降<0.02）几乎没有下降的情况下实现的。基于SHAP的可解释性显示，该框架减少了对性别代理特征的依赖，为临床医生提供了关于如何和在哪里纠正偏见的可操作见解。FairMed-XGB为公平的临床决策提供了稳健、可解释和伦理相符的解决方案，为在高风险医疗环境中可信地部署AI铺平了道路。
英文摘要：
Machine learning models deployed in critical care settings exhibit demographic biases, particularly gender disparities, that undermine clinical trust and equitable treatment. This paper introduces FairMed-XGB, a novel framework that systematically detects and mitigates gender-based prediction bias while preserving model performance and transparency. The framework integrates a fairness-aware loss function combining Statistical Parity Difference, Theil Index, and Wasserstein Distance, jointly optimised via Bayesian Search into an XGBoost classifier. Post-mitigation evaluation on seven clinically distinct cohorts derived from the MIMIC-IV-ED and eICU databases demonstrates substantial bias reduction: Statistical Parity Difference decreases by 40 to 51 percent on MIMIC-IV-ED and 10 to 19 percent on eICU; Theil Index collapses by four to five orders of magnitude to near-zero values; Wasserstein Distance is reduced by 20 to 72 percent. These gains are achieved with negligible degradation in predictive accuracy (AUC-ROC drop <0.02). SHAP-based explainability reveals that the framework diminishes reliance on gender-proxy features, providing clinicians with actionable insights into how and where bias is corrected. FairMed-XGB offers a robust, interpretable, and ethically aligned solution for equitable clinical decision-making, paving the way for trustworthy deployment of AI in high-stakes healthcare environments.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
