ReviewNeural regeneration research2018
How random is the random forest? Random forest algorithm on the service of structural imaging biomarkers for Alzheimer's disease: from Alzheimer's disease neuroimaging initiative (ADNI) database.
Review in Neural regeneration research, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 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
35 citing papers in PubMed, 79 citations in OpenAlex.
- Universum Inspired Graph Contrast Learning for Major Depressive Disorder Identification: In Functional Brain Network View.CNS neuroscience & therapeutics · 2026Article
- Comparative Analysis by Machine Learning of Geriatric Frailty and Alzheimer's Disease Classification Using Independent Datasets.Life (Basel, Switzerland) · 2026Article
- Neuroimaging and machine learning fusion for improved brain tumor diagnosis and prognosis.Scientific reports · 2026Article
- Women's Cardiovascular Disease and Stroke Risk Stratification Using a Precision and Personalized Framework Embedded with an Explainable Artificial Intelligence Paradigm: A Narrative Review.Diagnostics (Basel, Switzerland) · 2026Review
- Article
- Enhanced risk prediction of femoral head osteonecrosis in the elderly: a comparative study of random forest and logistic regression models.Frontiers in medicine · 2025Article
- INTegRated InterveNtion of pSychogerIatric Care: real-world application and implementation of an advanced integrated telehealth system incorporating machine learning.Frontiers in psychology · 2025Article
- A Fuzzy Cognitive Map-based Framework for Alzheimer's Disease Diagnosis Using Multimodal Magnetic Resonance Imaging-Positron Emission Tomography Registration.Journal of medical signals and sensors · 2025Article
- Integrative machine learning and Mendelian randomization identify causal laboratory biomarkers for coronary artery lesions in Kawasaki disease: a prospective study.Frontiers in genetics · 2025Article
- Comparing machine learning classifier models in discriminating cognitively unimpaired older adults from three clinical cohorts in the Alzheimer's disease spectrum: demonstration analyses in the COMPASS-ND study.Frontiers in aging neuroscience · 2025Article
- Using Machine Learning to Study Factors Affecting Discharge Destination in Recovery Units.Cureus · 2024Article
- Utilizing radiomics for differential diagnosis of inverted papilloma and chronic rhinosinusitis with polyps based on unenhanced CT scans.Scientific reports · 2024Article
- Accurate detection of acute sleep deprivation using a metabolomic biomarker-A machine learning approach.Science advances · 2024Article
- Recognition of Conus species using a combined approach of supervised learning and deep learning-based feature extraction.PloS one · 2024Article
- Examining the diagnostic value of the mnemonic discrimination task for classification of cognitive status and amyloid-beta burden.Neuropsychologia · 2023Article
- Machine learning analyses identify multi-modal frailty factors that selectively discriminate four cohorts in the Alzheimer's disease spectrum: a COMPASS-ND study.BMC geriatrics · 2023Article
- A novel method for assessing cardiac function in patients with coronary heart disease based on wrist pulse analysis.Irish journal of medical science · 2023Article
- GIRUS-net: A Multimodal Deep Learning Model Identifying Imaging and Genetic Biomarkers Linked to Alzheimer's Disease Severity.Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference · 2023Article
- Development and application of random forest regression soft sensor model for treating domestic wastewater in a sequencing batch reactor.Scientific reports · 2023Article
- WebGIS-Based Real-Time Surveillance and Response System for Vector-Borne Infectious Diseases.International journal of environmental research and public health · 2023Article
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 at 2 institutions in 3 countries.
Funding
Abstract
Neuroinformatics is a fascinating research field that applies computational models and analytical tools to high dimensional experimental neuroscience data for a better understanding of how the brain functions or dysfunctions in brain diseases. Neuroinformaticians work in the intersection of neuroscience and informatics supporting the integration of various sub-disciplines (behavioural neuroscience, genetics, cognitive psychology, etc.) working on brain research. Neuroinformaticians are the pathway of information exchange between informaticians and clinicians for a better understanding of the outcome of computational models and the clinical interpretation of the analysis. Machine learning is one of the most significant computational developments in the last decade giving tools to neuroinformaticians and finally to radiologists and clinicians for an automatic and early diagnosis-prognosis of a brain disease. Random forest (RF) algorithm has been successfully applied to high-dimensional neuroimaging data for feature reduction and also has been applied to classify the clinical label of a subject using single or multi-modal neuroimaging datasets. Our aim was to review the studies where RF was applied to correctly predict the Alzheimer's disease (AD), the conversion from mild cognitive impairment (MCI) and its robustness to overfitting, outliers and handling of non-linear data. Finally, we described our RF-based model that gave us the 1
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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.