Equitable Allocation of Healthcare Resources with Fair Survival Models

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https://epubs.siam.org/doi/10.1137/1.9781611976700.22

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http://hdl.handle.net/11603/20209
 https://doi.org/10.1137/1.9781611976700.22

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Author/Creator ORCID

https://orcid.org/0000-0001-5276-5708
 https://orcid.org/0000-0002-5989-8543
 https://orcid.org/0000-0002-3995-339X
 https://orcid.org/0000-0003-0935-4182

Date

2020-10-14

Type of Work

conference papers and proceedings
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Citation of Original Publication

Kamrun Naher Keya, Rashidul Islam, Shimei Pan, Ian Stockwell and James R. Foulds, Equitable Allocation of Healthcare Resources with Fair Cox Models,https://arxiv.org/abs/2010.06820
Keya, Kamrun Naher, Rashidul Islam, Shimei Pan, Ian Stockwell, and James Foulds. “Equitable Allocation of Healthcare Resources with Fair Survival Models.” In Proceedings of the 2021 SIAM International Conference on Data Mining (SDM), 190–98. Proceedings. Society for Industrial and Applied Mathematics, 2021. https://doi.org/10.1137/1.9781611976700.22.

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Copyright © 2021 by SIAM

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Abstract

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 or independent of demographic information-based harmful stereotypes. In this work, we develop multiple fairness definitions for survival models and corresponding fair Cox proportional hazards models to ensure equitable allocation of healthcare resources. We demonstrate the utility of our methods in terms of fairness and predictive accuracy on two publicly available survival datasets.