Fenchel duality of Cox partial likelihood with an application in survival kernel learning.
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 op...
| Publicado en: | Artificial Intelligence in Medicine Vol. 116 |
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| Autores principales: | , , , , |
| Formato: | research Journal Article |
| Publicado: |
Elsevier B.V.
Jun2021
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=150359115&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 150359115 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 09333657 3HY jtl: Artificial Intelligence in Medicine issn: 09333657 maglogo: N pubinfo: dt: Jun2021 vid: 116 pid: 1004 pub: Elsevier B.V. artinfo: ui: 150359115 150359115 NLM34020756 150359115 10.1016/j.artmed.2021.102077 NLM34020756 150359115 ppct: 1 formats: tig: atl: Fenchel duality of Cox partial likelihood with an application in survival kernel learning. aug: au: Wilson, Christopher M. Li, Kaiqiao Sun, Qiang Kuan, Pei Fen Wang, Xuefeng affil: Department of Biostatistics and Bioinformatics, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL 33612, USA sug: subj: Melanoma Skin Neoplasms Cox Proportional Hazards Model Algorithms Human Artificial Intelligence Comparative Studies Multicenter Studies Evaluation Research Validation Studies ab: 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. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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