Distance-Metric Learning for Personalized Survival Analysis

被引:1
作者
Galetzka, Wolfgang [1 ]
Kowall, Bernd [1 ]
Jusi, Cynthia [2 ]
Huessler, Eva-Maria [1 ]
Stang, Andreas [1 ]
Pardo, Leandro
机构
[1] Univ Hosp Essen, Inst Med Informat Biometr & Epidemiol, D-45130 Essen, Germany
[2] Nisso Chem Europe GmbH, D-40212 Dusseldorf, Germany
关键词
survival analysis; machine learning; metric learning; kernel regression; personalized medicine; TREES;
D O I
10.3390/e25101404
中图分类号
O4 [物理学];
学科分类号
0702 ;
摘要
Personalized time-to-event or survival prediction with right-censored outcomes is a pervasive challenge in healthcare research. Although various supervised machine learning methods, such as random survival forests or neural networks, have been adapted to handle such outcomes effectively, they do not provide explanations for their predictions, lacking interpretability. In this paper, an alternative method for survival prediction by weighted nearest neighbors is proposed. Fitting this model to data entails optimizing the weights by learning a metric. An individual prediction of this method can be explained by providing the user with the most influential data points for this prediction, i.e., the closest data points and their weights. The strengths and weaknesses in terms of predictive performance are highlighted on simulated data and an application of the method on two different real-world datasets of breast cancer patients shows its competitiveness with established methods.
引用
收藏
页数:16
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