@inproceedings{14f098abd6f648bfbcf5f357d31ee4f3,
title = "Fair-DSP: Fair Dynamic Survival Prediction on Longitudinal Electronic Health Record",
abstract = "Scarce medical resources and highly transmissible diseases may overwhelm healthcare infrastructure. Fair allocation based on disease progression and fair distribution among all demographic groups is demanded by society. Surprisingly, there is little work quantifying and ensuring fairness in the context of dynamic survival prediction to equally allocate medical resources. In this study, we formulate individual and group fairness metrics in the context of dynamic survival analysis with time-dependent covariates, in order to provide the necessary foundations to quantitatively analyze the fairness in dynamic survival analysis. We further develop a fairness-aware learner (Fair-DSP) that is generic and can be applied to a dynamic survival prediction model. The proposed learner specifically accounts for time-dependent covariates to ensure accurate predictions while maintaining fairness on the individual or group level. We conduct quantitative experiments and sensitivity studies on the real-world clinical PBC dataset. The results demonstrate that the proposed fairness notations and debiasing algorithm are capable of guaranteeing fairness in the presence of accurate prediction.",
keywords = "Deep learning, Dynamic survival analysis, EHR, Fairness",
author = "Xin Huang and Xiangyang Meng and Ni Zhao and Wenbin Zhang and Jianwu Wang",
note = "Publisher Copyright: {\textcopyright} 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.; Big Data Analytics and Knowledge Discovery - 25th International Conference, DaWaK 2023, Proceedings ; Conference date: 28-08-2023 Through 30-08-2023",
year = "2023",
doi = "10.1007/978-3-031-39831-5_15",
language = "English (US)",
isbn = "9783031398308",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "149--157",
editor = "Robert Wrembel and Johann Gamper and Gabriele Kotsis and Ismail Khalil and Tjoa, {A Min}",
booktitle = "Big Data Analytics and Knowledge Discovery - 25th International Conference, DaWaK 2023, Proceedings",
address = "Germany",
}