优化计算机断层扫描预约调度中的多利益相关方公平性：基于预测的扫描与报告时长
A Predictive-Prescriptive Analytics Framework for Fair Computed Tomography Scheduling and Radiologist Workload Allocation
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中文总结 AI 辅助
本文针对CT随访调度中多利益相关方公平性缺失问题，提出预测-优化框架，结合ML模型与MILP模型，通过支配性约简策略提升效率，实验表明该方法可平衡患者需求与放射科医生工作量，XGBoost适配性最优。
中文摘要 AI 辅助
安排随访计算机断层扫描（CT）检查需平衡两个相互冲突的目标：将患者安排在尽可能接近其推荐检查日期的时间，同时确保放射科医生工作量的公平分配。现有方法多优化扫描仪利用率或患者等待时间，却忽略了报告活动以及平衡多利益相关方公平性的需求。本文提出一种面向公平性感知的随访CT调度的预测-优化框架：首先开发患者特定的机器学习（ML）模型，预测检查时长与报告时长；随后将这些预测结果嵌入多目标混合整数线性规划（MILP）模型，该模型通过词典式最小-最大公平准则，同时最小化患者偏好检查日期的偏差并平衡放射科医生的报告工作量。我们在ε-约束框架内推导了一种支配性约简性质，可大幅减少生成帕累托前沿所需的优化问题数量。基于真实世界急诊放射科数据的计算实验表明，仅允许患者1至2天的调度灵活性，就足以显著改善放射科医生间的工作量公平性，同时保障随访检查的及时获取；所提支配性约简策略可在不影响帕累托前沿的情况下消除大部分ε-约束评估；最后，通过下游优化遗憾评估预测模型，结果显示XGBoost为调度决策提供最有效支持，其性能优于那些在传统预测指标下预测误差更低的模型。
英文摘要
Scheduling follow-up Computed Tomography (CT) examinations requires balancing two competing objectives: assigning patients as close as possible to their recommended examination dates while ensuring an equitable distribution of radiologists' workload. Existing approaches optimize scanner utilization or patient waiting times, overlooking reporting activities and the need to balance fairness across multiple stakeholders. This paper proposes a predictive-prescriptive framework for fairness-aware follow-up CT scheduling. Patient-specific Machine Learning (ML) models are first developed to predict both examination and reporting durations. These predictions are then embedded into a multi-objective Mixed-Integer Linear Programming (MILP) model that simultaneously minimizes deviations from patients' preferred examination dates and balances radiologists' reporting workloads through a lexicographic min-max fairness criterion. We derive a dominance reduction property within an $\varepsilon$-constraint framework that substantially reduces the number of optimization problems required to generate the Pareto frontier. Computational experiments based on data from a real-world emergency radiology department show that allowing patients a scheduling flexibility of only one to two days is sufficient to substantially improve workload equity among radiologists while preserving timely access to follow-up examinations. The proposed dominance reduction strategy eliminates most $\varepsilon$-constraint evaluations without affecting the Pareto frontier. Finally, evaluating predictive models through downstream optimization regret demonstrates that XGBoost provides the most effective support for scheduling decisions, outperforming models that achieve lower prediction errors according to conventional predictive metrics.
