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Cited by 678Statistical Methods and Bayesian InferenceStatistical Methods and InferenceMachine Learning in Healthcare

A review on longitudinal data analysis with random forest

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Hu, Jianchang, Szymczak, Silke·2023·Briefings in Bioinformatics
DOI 10.1093/bib/bbad002PMC PMC10025446
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Abstract

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 categorized according to the data structures for which they are designed. We consider both univariate and multivariate response longitudinal data and further categorize the repeated measurements according to whether the time effect is relevant. Even though most extensions are proposed for low-dimensional data, some can be applied to high-dimensional data. 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.

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  • RF can effectively handle high-dimensional data while offering high predictive accuracy and strong parallel computing capability.

    “RF can effectively handle high-dimensional data while offering high predictive accuracy and strong parallel computing capability [115].”

    From Artificial intelligence-powered prediction of diabetic complications: from clinical data to molecular omics (2026) · cites this paper as [115]

    The Introduction (page not provided) says RF is suited to high-dimensional settings, and the Standard RF algorithm section describes its predictive power. The passages do not establish the claimed strong parallel computing capability. (LLM, conf=0.85)

    Experimental — accuracy not yet measured

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Cited by

Sorted by citations

Authors
Hu, J., & Szymczak, S.
Affiliations
University of Lübeck (DE)
Corresponding author
Szymczak, Silke
ORCID
Venue
Briefings in Bioinformatics
Publisher
Oxford University Press (OUP)
Published
2023-01-18
Volume
24
Issue
2
ISSN
  • 1467-5463 (ISSN-L)
  • 1477-4054
DOI
10.1093/bib/bbad002
PMC
PMC10025446
OpenAlex
W4317388187
Cited by
678
References
4
Citations by year
2026: 217 · 2025: 314 · 2024: 124 · 2023: 22 · 2022: 1
FWCI
28.34
Type
journal article
Language
en
Open access
Licence
Locations
  • Briefings in Bioinformatics (publishedVersion, cc-by-nc)
  • PubMed Central (publishedVersion, cc-by-nc)
  • PubMed (publishedVersion)
  • arXiv (Cornell University) (submittedVersion)
  • arXiv (Cornell University) (cc-by)
Funders
  • German Federal Ministry of Education and Research
  • e:Med Programme on Systems Medicine (01ZX1510)
Topics
  • Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Bayesian Inference
  • Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Inference
  • Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
Keywords
random forest, longitudinal analysis, prediction model, multivariate response, high-dimensional data, repeated measures, time effects, machine learning
MeSH terms
Data Analysis, Random Forest, Longitudinal Studies, Software

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