  Time-to-event analysis, also known as survival analysis, aims to predict the
time of occurrence of an event, given a set of features. One of the major
challenges in this area is dealing with censored data, which can make learning
algorithms more complex. Traditional methods such as Cox's proportional hazards
model and the accelerated failure time (AFT) model have been popular in this
field, but they often require assumptions such as proportional hazards and
linearity. In particular, the AFT models often require pre-specified parametric
distributional assumptions. To improve predictive performance and alleviate
strict assumptions, there have been many deep learning approaches for
hazard-based models in recent years. However, representation learning for AFT
has not been widely explored in the neural network literature, despite its
simplicity and interpretability in comparison to hazard-focused methods. In
this work, we introduce the Deep AFT Rank-regression model for Time-to-event
prediction (DART). This model uses an objective function based on Gehan's rank
statistic, which is efficient and reliable for representation learning. On top
of eliminating the requirement to establish a baseline event time distribution,
DART retains the advantages of directly predicting event time in standard AFT
models. The proposed method is a semiparametric approach to AFT modeling that
does not impose any distributional assumptions on the survival time
distribution. This also eliminates the need for additional hyperparameters or
complex model architectures, unlike existing neural network-based AFT models.
Through quantitative analysis on various benchmark datasets, we have shown that
DART has significant potential for modeling high-throughput censored
time-to-event data.
