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

arXiv: https://arxiv.org/abs/2208.04112 · HTML: https://mlsanity.com/paper/2208.04112/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 66 references that are in the map.
- First posted on arXiv 8 Aug 2022.

## Builds on (most cited first, 3 of 3)

- [Random forests for high-dimensional longitudinal data](https://mlsanity.com/paper/1901.11279/) (2019)
- [Random Forests on Distance Matrices for Imaging Genetics Studies](https://mlsanity.com/paper/1309.6158/) (2013)
- [Generalised Linear Model Trees with Global Additive Effects](https://mlsanity.com/paper/1612.07498/) (2016)

## Built on by (most cited first, 3 of 3)

- [Design of UAV flight state recognition and trajectory prediction system based on trajectory feature construction](https://mlsanity.com/paper/2401.15564/) (2024)
- [Prediction by Machine Learning Analysis of Genomic Data Phenotypic Frost Tolerance in Perccottus glenii](https://mlsanity.com/paper/2410.08867/) (2024)
- [Fubini Study geometry of representation drift in high dimensional data](https://mlsanity.com/paper/2602.02596/) (2026)

## Abstract (arXiv)

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.