A review on longitudinal data analysis with random forest in precision medicine

  title={A review on longitudinal data analysis with random forest in precision medicine},
  author={Jianchang Hu and Silke Szymczak},
  journal={Briefings in bioinformatics},
In longitudinal studies variables are measured repeatedly over time, leading to clustered and correlated observations. If the goal of the study is to develop prediction models, machine learning approaches such as the powerful random forest (RF) are often promising alternatives to standard statistical methods, especially in the context of high-dimensional data. In this paper, we review extensions of the standard RF method for the purpose of longitudinal data analysis. Extension methods are… 

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