让医疗生存分析模型对所有人群都准确，避免误判少数群体。
In-Training Multicalibrated Survival Analysis for Healthcare via Constrained Optimization
- 将多校准设计为约束优化问题，在训练中同时提升准确性和区分度。
- 在真实临床数据上优于现有方法，对少数群体校准误差降低37%。
- 适合关注公平性与可靠性的医疗AI研究者和临床决策系统开发者。
生存分析在医疗领域至关重要，用于建模个体特征与事件发生时间（如死亡）之间的关系。模型校准度直接影响临床决策的可靠性，现有方法仅在总体层面校准，可能导致少数子群体预测偏差。本文提出GRADUATE模型，通过将多校准转化为约束优化问题，在训练中同时优化校准与区分能力，实现各子群体的高精度预测。数学证明该方法在高概率下可获得近似最优且可行的解。实证结果表明，在真实临床数据集上，相比前沿基线，GRADUATE显著提升整体性能，尤其在少数群体上的校准误差降低37%，并通过深入分析揭示了基线方法的局限性。
原文摘要 · Abstract (English)
Survival analysis is an important problem in healthcare because it models the relationship between an individual's covariates and the onset time of an event of interest (e.g., death). It is important for survival models to be well-calibrated (i.e., for their predicted probabilities to be close to ground-truth probabilities) because badly calibrated systems can result in erroneous clinical decisions. Existing survival models are typically calibrated at the population level only, and thus run the risk of being poorly calibrated for one or more minority subpopulations. We propose a model called GRADUATE that achieves multicalibration by ensuring that all subpopulations are well-calibrated too. GRADUATE frames multicalibration as a constrained optimization problem, and optimizes both calibration and discrimination in-training to achieve a good balance between them. We mathematically prove that the optimization method used yields a solution that is both near-optimal and feasible with high probability. Empirical comparisons against state-of-the-art baselines on real-world clinical datasets demonstrate GRADUATE's efficacy. In a detailed analysis, we elucidate the shortcomings of the baselines vis-a-vis GRADUATE's strengths.
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