研究提出容量感知出院策略，减少医院再入院预测偏差
Learning through Capacity Awareness to Minimize Hospital Readmissions
医院再入院预测的偏差会随出院决策自我强化，这篇论文给出一个聪明的解法：用容量感知的出院策略让数据更丰富，不需要额外加公平性约束就能把误差降 24%-25%、成本差距缩 38%-63%。做医疗 AI 或运筹优化的人都该看看。
一篇 arXiv 论文（编号 2611.11493）针对医院出院决策中的再入院风险预测偏差问题，将排队论 Markov 决策过程扩展为包含周期性重训练的再入院风险预测器。作者的核心洞察是：成本最优的容量感知（capacity-aware）出院策略能自然地使患者住院时长多样化，从而在观测数据层面实现再入院风险的一致性估计，无需显式的公平性约束。
论文证明了在 discharge-up-to 策略下反复重训练会在良好设定条件下收敛到无偏风险估计。基于合作医院患者级数据的校准仿真中，常用的按风险阈值出院策略会保持持续偏差，而 discharge-up-to 策略相比现状决策策略将学习误差降低 24%-25%。
估计精度的提升同时缓解了部分偏差：在按人群基线风险差异调整后，该策略将再入院成本在人群间的差异缩小 38%-63%。这为医院通过运营策略本身改善预测性能与公平性提供了不依赖额外公平性约束的路径。
Learning through Capacity Awareness to Minimize Hospital Readmissions
Predictive algorithms are increasingly used to guide hospital discharge decisions, yet these models can exhibit censoring bias for vulnerable patients due to insufficient historical data or past discharge decisions. When biased readmission risk predictors are used to inform discharges, the training data generated by discharge decisions may perpetuate similar biases when predictors are re-trained, leading to a vicious cycle. We extend a queueing-based Markov decision process for hospital discharge to incorporate periodically retrained readmission risk predictors. Our key insight is that cost-optimal, "capacity-aware" operational decision policies can naturally diversify the lengths of stay at which patient outcomes are observed, enabling consistent estimation of readmission risk without explicit fairness constraints. We prove that repeated retraining under discharge-up-to policies converges to unbiased risk estimates under well-specification. In synthetic experiments and calibrated simulations based on patient-level data from a partner hospital, we find that where commonly used threshold-on-risk policies can remain persistently biased, discharge-up-to policies reduce learning error over status quo decision policies (such as thresholding on risk) by 24%-25%. These improvements to estimation also mitigate some forms of bias, reducing readmission cost disparities (adjusted for baseline risk differences between demographic groups) by 38%-63%. Our results demonstrate how capacity-aware decision policies can improve both predictive performance and fairness by addressing censoring bias.
This report is categorized under AITOP's "Papers" desk, focusing on US frontier AI labs and Silicon Valley technology leaders. Within our cross-regional analytical framework, it directly maps to AI developer toolchains, coding agents, and engineering workflows. Key entities and technical topics under continuous tracking include 再入院预测, 公平性, Markov决策过程, 医疗AI.
Source attribution: curated from arXiv cs.LG (@Lydia T. Liu, Kara Schechtman, Pengyi Shi, Angela Zhou).