  Recurrent events are common in clinical, healthcare, social and behavioral
studies. A recent analysis framework for potentially censored recurrent event
data is to construct a censored longitudinal data set consisting of times to
the first recurrent event in multiple prespecified follow-up windows of length
$\tau$. With the staggering number of potential predictors being generated from
genetic, -omic, and electronic health records sources, machine learning
approaches such as the random forest are growing in popularity, as they can
incorporate information from highly correlated predictors with non-standard
relationships. In this paper, we bridge this gap by developing a random forest
approach for dynamically predicting probabilities of remaining event-free
during a subsequent $\tau$-duration follow-up period from a reconstructed
censored longitudinal data set. We demonstrate the increased ability of our
random forest algorithm for predicting the probability of remaining event-free
over a $\tau$-duration follow-up period when compared to the recurrent event
modeling framework of Xia et al. (2020) in settings where association between
predictors and recurrent event outcomes is complex in nature. The proposed
random forest algorithm is demonstrated using recurrent exacerbation data from
the Azithromycin for the Prevention of Exacerbations of Chronic Obstructive
Pulmonary Disease (Albert et al., 2011).
