ReviewJournal of minimally invasive surgery2024
Automated machine learning with R: AutoML tools for beginners in clinical research.
Review in Journal of minimally invasive surgery, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 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
3 citing papers in PubMed.
- Clinician-accessible automated machine learning in oral healthcare: A systematic review.The Japanese dental science review · 2026Review
- Q-MIND: enhancing ADHD diagnosis using quantum machine learning for advanced neuroimaging analysis.Journal of neural transmission (Vienna, Austria : 1996) · 2025Article
- Risk Prediction of Liver Injury in Pediatric Tuberculosis Treatment: Development of an Automated Machine Learning Model.Drug design, development and therapy · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Recently, interest in machine learning (ML) has increased as the application fields have expanded significantly. Although ML methods excel in many fields, establishing an ML pipeline requires considerable time and human resources. Automated ML (AutoML) tools offer a solution by automating repetitive tasks, such as data preprocessing, model selection, hyperparameter optimization, and prediction analysis. This review introduces the use of AutoML tools for general research, including clinical studies. In particular, it outlines a simple approach that is accessible to beginners using the R programming language (R Foundation for Statistical Computing). In addition, the practical code and output results for binary classification are provided to facilitate direct application by clinical researchers in future studies.
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.