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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Keya, K.N., Islam, R., Pan, S., Stockwell, I., Foulds, I.: Equitable allocation of healthcare resources with fair survival models. In: Proceedings of the 2021 SIAM International Conference on Data Mining (SDM), pp. 190–198. Society for Industrial and Applied Mathematics (2021)
Lee, C., Yoon, J., Van Der Schaar, M.: Dynamic-DEEPHIT: a deep learning approach for dynamic survival analysis with competing risks based on longitudinal data. IEEE Trans. Biomed. Eng. 67(1), 122–133 (2020)
Huang, X., et al.: A Generic knowledge based medical diagnosis expert system. In: The 23rd International Conference on Information Integration and Web Intelligence (2021)
Cox, D.R.: Regression models and life tables (with discussion). J R Statist Soc B 34, 187–220 (1972)
Zhang, W., Weiss, J.: Fair Decision-making Under Uncertainty. In: ICDM (2021)
Katzman, J., Shaham, U., Cloninger, A., Bates, J., Jiang, T., Kluger, Y.: DeepSurv: personalized treatment recommender system using a Cox proportional hazards deep neural network. BMC Med. Res. Methodol. 18(1), 24 (2018)
Lee, C., Zame, W., Yoon, I., van der Schaar, M.: DeepHit: a deep learning approach to survival analysis with competing risks. In: Thirty-Second AAAI Conference on Artificial Intelligence (2018)
Rava, D., Bradic, J.: DeepHazard: neural network for time-varying risks. ArXiv, abs/2007.13218 (2020)
Chen, C., Wong, R.: Black patients miss out on promising cancer drugs-propublica. 2018 (2019)
Dwork, C., Hardt, M., Pitassi, T., Reingold, Q., Zemel, R.: Fairness through awareness. In: Proceedings of the 3rd Innovations in Theoretical Computer Science Conference, pp. 214–226 (2012)
Kamiran, F., Calders, T.: Data preprocessing techniques for classification without discrimination. Knowl. Inf. Syst. 33, 1–33 (2012)
Barocas, S., Hardt, M., Narayanan, A.: Fairness in machine learning. Knowledge and Information Systems, Nips tutorial 1, 2 (2017)
Esquivel, C.Q., et al.: Transplantation for primary biliary cirrhosis. Gastroenterology 94, 1207–1216 (1988)
Graf, E., Schmoor, C., Schumacher, M.: Assessment and comparison of prognostic classification schemes for survival data. Stat. Med. 18(17–18) (1999)
V. Raykar, H. Steck, B. Krishnapuram, C. Dehing-Oberije, and P. Lambin. On ranking in survival analysis: Bounds on the concordance index. Proceedings of the Twenty-First Annual Conference on Neural Information Processing Systems, Vancouver, British Columbia, Canada, December 3–6, 2007, 1209–1216
Zhang, W., Weiss, J.: Longitudinal Fairness with Censorship. In: Proceedings of the 36th AAAI Conference on Artificial Intelligence (AAAI), online (2022)
Rahman, M., Purushotham, S.: Fair and Interpretable Models for Survival Analysis of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2022), pp. 1452–1462 (2022)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Huang, X., Meng, X., Zhao, N., Zhang, W., Wang, J. (2023). Fair-DSP: Fair Dynamic Survival Prediction on Longitudinal Electronic Health Record. In: Wrembel, R., Gamper, J., Kotsis, G., Tjoa, A.M., Khalil, I. (eds) Big Data Analytics and Knowledge Discovery. DaWaK 2023. Lecture Notes in Computer Science, vol 14148. Springer, Cham. https://doi.org/10.1007/978-3-031-39831-5_15
Download citation
DOI: https://doi.org/10.1007/978-3-031-39831-5_15
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-39830-8
Online ISBN: 978-3-031-39831-5
eBook Packages: Computer ScienceComputer Science (R0)