ArticleStatistics in medicine2015
Random forest classification of etiologies for an orphan disease.
Article in Statistics in medicine, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT00518440 (A Multi-Center Trial to Study Acute Liver Failure in Adults), which is not on this map. Cited by 22 papers, 1 of them a synthesis that pooled it.
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.
A Multi-Center Trial to Study Acute Liver Failure in Adults
Who cites it
22 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Pooled it
- Comparing the use of supervised machine learning variable selection methods in the context of two-group classification in the psychological and health sciences.Frontiers in psychology · 2026Article
- Use of pulse pressure index for cardiovascular outcomes assessment and development of a coronary heart disease model for the elderly.BMC cardiovascular disorders · 2025Article
- A comparison of random forest variable selection methods for regression modeling of continuous outcomes.Briefings in bioinformatics · 2025Article
- Relationships Between Chemical Compounds and Sensory Properties of Virgin Olive Oil in the US and Israel: Development of a Prediction Model for Defects.Journal of agricultural and food chemistry · 2024Article
- Exploration of the Shared Hub Genes and Biological Mechanism in Osteoporosis and Type 2 Diabetes Mellitus based on Machine Learning.Biochemical genetics · 2023Article
- Efficacy of Intravenous Elosulfase Alfa for Mucopolysaccharidosis Type IVA: A Systematic Review and Meta-Analysis.Journal of personalized medicine · 2022Article
- Identification of Potential Ferroptosis-Related Biomarkers and Immune Infiltration in Human Coronary Artery Atherosclerosis.International journal of general medicine · 2022Article
- Article
- A random forest method with feature selection for developing medical prediction models with clustered and longitudinal data.Journal of biomedical informatics · 2021Article
- Machine Learning in Aging: An Example of Developing Prediction Models for Serious Fall Injury in Older Adults.The journals of gerontology. Series A, Biological sciences and medical sciences · 2021Article
- Identification of Ten Core Hub Genes as Potential Biomarkers and Treatment Target for Hepatoblastoma.Frontiers in oncology · 2021Article
- Article
- Identification of key genes and immune infiltration modulated by CPAP in obstructive sleep apnea by integrated bioinformatics analysis.PloS one · 2021Article
- Developing and validating a multivariable prediction model for in-hospital mortality of pneumonia with advanced chronic kidney disease patients: a retrospective analysis using a nationwide database in Japan.Clinical and experimental nephrology · 2020Article
- Application of data mining for predicting hemodynamics instability during pheochromocytoma surgery.BMC medical informatics and decision making · 2020Article
- A Comparison of Random Forest Variable Selection Methods for Classification Prediction Modeling.Expert systems with applications · 2019Article
- Machine learning applications in epilepsy.Epilepsia · 2019Review
- BiMM forest: A random forest method for modeling clustered and longitudinal binary outcomes.Chemometrics and intelligent laboratory systems : an international journal sponsored by the Chemometrics Society · 2019Article
- The Natural History of Severe Acute Liver Injury.The American journal of gastroenterology · 2017Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
Classification of objects into pre-defined groups based on known information is a fundamental problem in the field of statistics. Although approaches for solving this problem exist, finding an accurate classification method can be challenging in an orphan disease setting, where data are minimal and often not normally distributed. The purpose of this paper is to illustrate the application of the random forest (RF) classification procedure in a real clinical setting and discuss typical questions that arise in the general classification framework as well as offer interpretations of RF results. This paper includes methods for assessing predictive performance, importance of predictor variables, and observation-specific information.
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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.