Precision medicine provides customized treatments to patients based on their
characteristics and is a promising approach to improving treatment efficiency.
Large scale omics data are useful for patient characterization, but often their
measurements change over time, leading to longitudinal data. Random forest is
one of the state-of-the-art machine learning methods for building prediction
models, and can play a crucial role in precision medicine. In this paper, we
review extensions of the standard random forest method for the purpose of
longitudinal data analysis. Extension methods are categorized according to the
data structures for which they are designed. We consider both univariate and
multivariate responses and further categorize the repeated measurements
according to whether the time effect is relevant. Information of available
software implementations of the reviewed extensions is also given. We conclude
with discussions on the limitations of our review and some future research
directions.Comment: 27 pages, 2 figures, 3 table