Harmonic-Mean Cox Models: A Ruler for Equal Attention to Risk
Proceedings of AAAI Spring Symposium on Survival Prediction - Algorithms, Challenges, and Applications 2021, PMLR 146:171-183, 2021.
Survival analysis models are necessary for clinical forecasting with data censorship. Implicitly, existing works focus on the individuals with higher risks while lower risk individuals are poorly characterized. Developing survival models to represent different risk individuals equally is a challenging task but of great importance for providing accurate risk assessments across levels of risk. Here, we characterize this problem and propose an adjusted log-likelihood formulation as the new objective for survival prognostication. Several models are then proposed based on the newly designed optimization objective function which produce risks that count individuals “equally” on risk ratios thus providing representative attention to individuals of varying risk. Extensive experiments on multiple real-world datasets demonstrate the benefits of the proposed approach.