ArticleArtificial intelligence in medicine2021
Fenchel duality of Cox partial likelihood with an application in survival kernel learning.
Article in Artificial intelligence in medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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4 citing papers in PubMed.
- Survival parametric modeling for patients with heart failure based on Kernel learning.BMC medical research methodology · 2025Article
- Predicting prognosis for epithelial ovarian cancer patients receiving bevacizumab treatment with CT-based deep learning.NPJ precision oncology · 2024Article
- Current status of artificial intelligence methods for skin cancer survival analysis: a scoping review.Frontiers in medicine · 2024Article
- Statistical and Machine Learning Methods for Discovering Prognostic Biomarkers for Survival Outcomes.Methods in molecular biology (Clifton, N.J.) · 2023Article
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5 authors.
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Abstract
The Cox proportional hazard model is one of the most widely used methods in modeling time-to-event data in the health sciences. Due to the simplicity of the Cox partial likelihood function, many machine learning algorithms use it for survival data. However, due to the nature of censored data, the optimization problem becomes intractable when more complicated regularization is employed, which is necessary when dealing with high dimensional omic data. In this paper, we show that a convex conjugate function of the Cox loss function based on Fenchel duality exists, and provide an alternative framework to optimization based on the primal form. Furthermore, the dual form suggests an efficient algorithm for solving the kernel learning problem with censored survival outcomes. We illustrate performance and properties of the derived duality form of Cox partial likelihood loss in multiple kernel learning problems with simulated and the Skin Cutaneous Melanoma TCGA datasets.
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