English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Application of discrete-time survival methods for continuous-time survivalprediction is considered. For this purpose, a scheme for discretization ofcontinuous-time data is proposed by considering the quantiles of the estimatedevent-time distribution, and, for smaller data sets, it is found to bepreferable over the commonly used equidistant scheme. Furthermore, twointerpolation schemes for continuous-time survival estimates are explored, bothof which are shown to yield improved performance compared to the discrete-timeestimates. The survival methods considered are based on the likelihood forright-censored survival data, and parameterize either the probability massfunction (PMF) or the discrete-time hazard rate, both with neural networks.Through simulations and study of real-world data, the hazard rateparametrization is found to perform slightly better than the parametrization ofthe PMF. Inspired by these investigations, a continuous-time method is proposedby assuming that the continuous-time hazard rate is piecewise constant. Themethod, named PC-Hazard, is found to be highly competitive with theaforementioned methods in addition to other methods for survival predictionfound in the literature.

Continuous and Discrete-Time Survival Prediction with Neural Networks