Random survival forests for competing risks with multivariate longitudinal endogenous covariates. (arXiv:2208.05801v1 [stat.ML])

Predicting the individual risk of a clinical event using the complete patient
history is still a major challenge for personalized medicine. Among the methods
developed to compute individual dynamic predictions, the joint models have the
assets of using all the available information while accounting for dropout.
However, they are restricted to a very small number of longitudinal predictors.
Our objective was to propose an innovative alternative solution to predict an
event probability using a possibly large number of longitudinal predictors. …

arxiv endogenous ml random risks survival