Equitable Survival Prediction: A Fairness-Aware Survival Modeling (FASM) Approach
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arXiv
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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866908607483740160 |
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| author | Liu, Mingxuan Ning, Yilin Wang, Haoyuan Hong, Chuan Engelhard, Matthew Bitterman, Danielle S. La Cava, William G. Liu, Nan |
| author_facet | Liu, Mingxuan Ning, Yilin Wang, Haoyuan Hong, Chuan Engelhard, Matthew Bitterman, Danielle S. La Cava, William G. Liu, Nan |
| contents | As machine learning models become increasingly integrated into healthcare, structural inequities and social biases embedded in clinical data can be perpetuated or even amplified by data-driven models. In survival analysis, censoring and time dynamics can further add complexity to fair model development. Additionally, algorithmic fairness approaches often overlook disparities in cross-group rankings, e.g., high-risk Black patients may be ranked below lower-risk White patients who do not experience the event of mortality. Such misranking can reinforce biological essentialism and undermine equitable care. We propose a Fairness-Aware Survival Modeling (FASM), designed to mitigate algorithmic bias regarding both intra-group and cross-group risk rankings over time. Using breast cancer prognosis as a representative case and applying FASM to SEER breast cancer data, we show that FASM substantially improves fairness while preserving discrimination performance comparable to fairness-unaware survival models. Time-stratified evaluations show that FASM maintains stable fairness over a 10-year horizon, with the greatest improvements observed during the mid-term of follow-up. Our approach enables the development of survival models that prioritize both accuracy and equity in clinical decision-making, advancing fairness as a core principle in clinical care. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_20629 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Equitable Survival Prediction: A Fairness-Aware Survival Modeling (FASM) Approach Liu, Mingxuan Ning, Yilin Wang, Haoyuan Hong, Chuan Engelhard, Matthew Bitterman, Danielle S. La Cava, William G. Liu, Nan Machine Learning Artificial Intelligence As machine learning models become increasingly integrated into healthcare, structural inequities and social biases embedded in clinical data can be perpetuated or even amplified by data-driven models. In survival analysis, censoring and time dynamics can further add complexity to fair model development. Additionally, algorithmic fairness approaches often overlook disparities in cross-group rankings, e.g., high-risk Black patients may be ranked below lower-risk White patients who do not experience the event of mortality. Such misranking can reinforce biological essentialism and undermine equitable care. We propose a Fairness-Aware Survival Modeling (FASM), designed to mitigate algorithmic bias regarding both intra-group and cross-group risk rankings over time. Using breast cancer prognosis as a representative case and applying FASM to SEER breast cancer data, we show that FASM substantially improves fairness while preserving discrimination performance comparable to fairness-unaware survival models. Time-stratified evaluations show that FASM maintains stable fairness over a 10-year horizon, with the greatest improvements observed during the mid-term of follow-up. Our approach enables the development of survival models that prioritize both accuracy and equity in clinical decision-making, advancing fairness as a core principle in clinical care. |
| title | Equitable Survival Prediction: A Fairness-Aware Survival Modeling (FASM) Approach |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2510.20629 |