  Scoring systems are highly interpretable and widely used to evaluate
time-to-event outcomes in healthcare research. However, existing time-to-event
scores are predominantly created ad-hoc using a few manually selected variables
based on clinician's knowledge, suggesting an unmet need for a robust and
efficient generic score-generating method.
  AutoScore was previously developed as an interpretable machine learning score
generator, integrated both machine learning and point-based scores in the
strong discriminability and accessibility. We have further extended it to
time-to-event data and developed AutoScore-Survival, for automatically
generating time-to-event scores with right-censored survival data. Random
survival forest provides an efficient solution for selecting variables, and Cox
regression was used for score weighting. We illustrated our method in a
real-life study of 90-day mortality of patients in intensive care units and
compared its performance with survival models (i.e., Cox) and the random
survival forest.
  The AutoScore-Survival-derived scoring model was more parsimonious than
survival models built using traditional variable selection methods (e.g.,
penalized likelihood approach and stepwise variable selection), and its
performance was comparable to survival models using the same set of variables.
Although AutoScore-Survival achieved a comparable integrated area under the
curve of 0.782 (95% CI: 0.767-0.794), the integer-valued time-to-event scores
generated are favorable in clinical applications because they are easier to
compute and interpret.
  Our proposed AutoScore-Survival provides an automated, robust and easy-to-use
machine learning-based clinical score generator to studies of time-to-event
outcomes. It provides a systematic guideline to facilitate the future
development of time-to-event scores for clinical applications.
