  An every increasing number of clinical trials features a time-to-event
outcome and records non-tabular patient data, such as magnetic resonance
imaging or text data in the form of electronic health records. Recently,
several neural-network based solutions have been proposed, some of which are
binary classifiers. Parametric, distribution-free approaches which make full
use of survival time and censoring status have not received much attention. We
present deep conditional transformation models (DCTMs) for survival outcomes as
a unifying approach to parametric and semiparametric survival analysis. DCTMs
allow the specification of non-linear and non-proportional hazards for both
tabular and non-tabular data and extend to all types of censoring and
truncation. On real and semi-synthetic data, we show that DCTMs compete with
state-of-the-art DL approaches to survival analysis.
