Equitable Allocation of Healthcare Resources with Fair Survival Models
Equitable Allocation of Healthcare Resources with Fair Survival Models
复制标题
以公平生存模式公平分配医疗资源
DOI：
10.1137/1.9781611976700.22
复制
发表时间：
2021
期刊：
影响因子：
--
通讯作者：
Foulds, James
中科院分区：
文献类型：
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作者：
Keya, Kamrun Naher;Islam, Rashidul;Pan, Shimei;Stockwell, Ian;Foulds, James
医疗补助等医疗保健计划为弱势群体提供了重要的服务，但由于资源有限，许多最需要这些服务的人在等待名单上苦苦挣扎。生存模型，例如考克斯比例风险模型，可以通过预测个人的需求水平来潜在地改善这种情况，然后可以使用这种模型来确定等待名单的优先顺序。向有需要的人提供护理可以防止这些人被送入机构，这既提高了生活质量，又降低了总体成本。虽然这种做法的好处是显而易见的，但必须注意确保确定优先次序的过程是公平的，不会强化有害的系统性偏见。我们为生存模型开发了多个公平性定义和相应的公平学习算法，以确保医疗资源的公平分配。我们证明了我们的方法在三个公开的生存数据集的公平性和预测准确性方面的实用性。
Healthcare programs such as Medicaid provide crucial services to vulnerable populations, but due to limited resources, many of the individuals who need these services the most languish on waiting lists. Survival models, e.g. the Cox proportional hazards model, can potentially improve this situation by predicting individuals' levels of need, which can then be used to prioritize the waiting lists. Providing care to those in need can prevent institutionalization for those individuals, which both improves quality of life and reduces overall costs. While the benefits of such an approach are clear, care must be taken to ensure that the prioritization process is fair, and does not reinforce harmful systemic bias. We develop multiple fairness definitions and corresponding fair learning algorithms for survival models to ensure equitable allocation of healthcare resources. We demonstrate the utility of our methods in terms of fairness and predictive accuracy on three publicly available survival datasets.