开发公平性感知的任务分解以提高脊柱融合术后并发症预测的公平性
Developing Fairness-Aware Task Decomposition to Improve Equity in Post-Spinal Fusion Complication Prediction
AI总结：
FAIR-MTL通过公平性感知的多任务学习框架，提升脊柱融合术后并发症预测的公平性和可解释性。
摘要：
临床预测模型中的公平性仍然是一个持续存在的挑战，特别是在高风险应用中，如脊柱融合手术治疗脊柱侧弯，其中患者结果表现出显著的异质性。许多现有的公平性方法依赖于粗略的种族调整或事后修正，这些方法无法捕捉临床人群的潜在结构，可能无意中强化偏见。我们提出了FAIR-MTL，一种公平性感知的多任务学习框架，旨在提供公平且细致的术后并发症严重程度预测。与在模型训练中依赖显式敏感属性不同，FAIR-MTL采用数据驱动的子组推断机制。我们提取了一个紧凑的种族嵌入，并应用k-means聚类来揭示可能受传统模型影响差异的潜在患者子组。这些推断出的子组标签决定了共享多任务架构中的任务路由。在训练过程中，通过反频率加权缓解子组不平衡，并通过正则化防止过度拟合较小的子组。应用于具有四个严重程度级别的术后并发症预测，FAIR-MTL实现了AUC为0.86和准确率为75%，优于单任务基线，同时显著减少了偏见。对于性别，人口平差差异降低到0.055，相等机会降低到0.094；对于年龄，这些值分别降低到0.056和0.148。通过SHAP和Gini重要性分析确保模型可解释性，这些分析一致突出了临床有意义的预测因子，如血红蛋白、血细胞比容和患者体重。我们的发现表明，将无监督的子组发现纳入多任务框架中，可以实现更加公平、可解释且具有临床可行性的手术风险分层预测。
英文摘要：
Fairness in clinical prediction models remains a persistent challenge, particularly in high-stakes applications such as spinal fusion surgery for scoliosis, where patient outcomes exhibit substantial heterogeneity. Many existing fairness approaches rely on coarse demographic adjustments or post-hoc corrections, which fail to capture the latent structure of clinical populations and may unintentionally reinforce bias. We propose FAIR-MTL, a fairness-aware multitask learning framework designed to provide equitable and fine-grained prediction of postoperative complication severity. Instead of relying on explicit sensitive attributes during model training, FAIR-MTL employs a data-driven subgroup inference mechanism. We extract a compact demographic embedding, and apply k-means clustering to uncover latent patient subgroups that may be differentially affected by traditional models. These inferred subgroup labels determine task routing within a shared multitask architecture. During training, subgroup imbalance is mitigated through inverse-frequency weighting, and regularization prevents overfitting to smaller groups. Applied to postoperative complication prediction with four severity levels, FAIR-MTL achieves an AUC of 0.86 and an accuracy of 75%, outperforming single-task baselines while substantially reducing bias. For gender, the demographic parity difference decreases to 0.055 and equalized odds to 0.094; for age, these values reduce to 0.056 and 0.148, respectively. Model interpretability is ensured through SHAP and Gini importance analyses, which consistently highlight clinically meaningful predictors such as hemoglobin, hematocrit, and patient weight. Our findings show that incorporating unsupervised subgroup discovery into a multitask framework enables more equitable, interpretable, and clinically actionable predictions for surgical risk stratification.
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