Distance-Metric Learning for Personalized Survival Analysis

Autor: Wolfgang Galetzka, Bernd Kowall, Cynthia Jusi, Eva-Maria Huessler, Andreas Stang
Jazyk: angličtina
Rok vydání: 2023
Předmět:
Zdroj: Entropy, Vol 25, Iss 10, p 1404 (2023)
Druh dokumentu: article
ISSN: 25101404
1099-4300
DOI: 10.3390/e25101404
Popis: 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.
Databáze: Directory of Open Access Journals
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