  In many longitudinal studies, the covariate and response are often
intermittently observed at irregular, mismatched and subject-specific times.
How to deal with such data when covariate and response are observed
asynchronously is an often raised problem. Bayesian Additive Regression
Trees(BART) is a Bayesian non-Parametric approach which has been shown to be
competitive with the best modern predictive methods such as random forest and
boosted decision trees. The sum of trees structure combined with a Bayesian
inferential framework provide a accurate and robust statistic method. BART
variant soft Bayesian Additive Regression Trees(SBART) constructed using
randomized decision trees was developed and substantial theoretical and
practical benefits were shown. In this paper, we propose a weighted SBART model
solution for asynchronous longitudinal data. In comparison to other methods,
the current methods are valid under with little assumptions on the covariate
process. Extensive simulation studies provide numerical support for this
solution. And data from an HIV study is used to illustrate our methodology
