ReviewBriefings in bioinformatics2023
A review on longitudinal data analysis with random forest.
Review in Briefings in bioinformatics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 323 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
323 citing papers in PubMed.
- Development and validation of a machine learning-based prediction model for prolonged length of stay after laparoscopic gastrointestinal surgery: a secondary analysis of the FDP-PONV trial.BMC gastroenterology · 2025Trial
- Construction and validation of a machine learning model for predicting urine culture positivity in children with suspected urinary tract infections.Annals of medicine · 2026Article
- Who Is Getting the Help They Need? An AI-Driven Study of Intersectional Disparities in Mental Health Service Utilization Among Young Adults with Suicidal Ideation.Prevention science : the official journal of the Society for Prevention Research · 2026Article
- PiProteline: An R Package for Integrated Proteomics Data Analysis, from Label-Free Quantitation to Systems Biology.Biology · 2026Article
- Development and Validation of a Machine Learning-Based Model for Predicting All-Cause Mortality Risk in Patients With Type 2 Diabetes Mellitus Combined With Hypertension: National Cohort Study.JMIR medical informatics · 2026Article
- A clinical scoring system for the early identification of superinfections in patients with severe fever with thrombocytopenia syndrome: An ambispective multicentre cohort study.PLoS neglected tropical diseases · 2026Article
- Evaluation of explainable machine learning models for predicting mid-term stone recurrence after percutaneous nephrolithotomy: a retrospective observational cohort study.International urology and nephrology · 2026Observational
- Preoperative Serum Alkaline Phosphatase Predicts Arteriovenous Fistula Maturation Failure.Kidney international reports · 2026Article
- Development and Validation of an Interpretable Machine Learning Model for Predicting ICU-Acquired Weakness in Postoperative Patients.Nursing in critical care · 2026Article
- Identification of angiogenesis-related genes in the diagnosis of benign prostatic hyperplasia using bioinformatics analysis.Translational andrology and urology · 2026Article
- Metabolic biomarkers of treatment-resistant schizophrenia and cognitive correlates: insights from a machine learning.Molecular psychiatry · 2026Article
- Article
- Cardiopulmonary Exercise Testing and Exercise Stress Echocardiography in Degenerative Mitral Regurgitation: An Exploratory Analysis of Functional and Left Ventricular Outcomes After Surgery.Journal of cardiovascular development and disease · 2026Article
- MRI-Based Radiomics Model for Predicting Eligibility for Conservative Management of Endometrial Cancer.Cancer medicine · 2026Article
- Artificial Intelligence-Based Delirium Prediction Model for Post-Cardiac Surgery Patients: A Scoping Review.Journal of advanced nursing · 2026Article
- A lactylation-related transcriptional signature as candidate biomarkers for the early detection of polycystic ovary syndrome: an integrated genomic analysis.Journal of ovarian research · 2026Article
- An ensemble machine learning approach for predicting colorectal polyps using novel inflammatory markers.BMC gastroenterology · 2026Article
- Recognition Analysis of Ginseng Radix et Rhizoma and Confused Varieties according to Chemometrics and Machine Learning.ACS omega · 2026Article
- Single-cell and spatial transcriptomics identify CAPG as a key driver of cisplatin resistance in bladder cancer.Functional & integrative genomics · 2026Article
- Prediction Models for Postoperative Delirium Among Cancer Patients: A Scoping Review.Healthcare (Basel, Switzerland) · 2026Review
263 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.