ReviewComputational and mathematical methods in medicine2017
An Update on Statistical Boosting in Biomedicine.
Review in Computational and mathematical methods in medicine, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 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
13 citing papers in PubMed.
- Balancing Savanna Ungulate Diversity and Biomass: Optimal Human Use, Landscape Features, and Vegetation Types Under Varying Rainfall and Land Use.Ecology and evolution · 2026Article
- Modelling dependent censoring in time-to-event data using boosting copula regression.Lifetime data analysis · 2025Article
- Comparative Ungulate Diversity and Biomass Change With Human Use and Drought: Implications for Community Stability and Protected Area Prioritization in African Savannas.Ecology and evolution · 2025Article
- Photoplethysmography based non-invasive blood glucose estimation using systolic-diastolic framing MFCC features and machine learning regression.BioImpacts : BI · 2025Article
- Evaluation of Neoplasia, Treatments, and Survival in Lizard Species.Animals : an open access journal from MDPI · 2024Article
- Adaptive monitoring in action-what drives arthropod diversity and composition in central European beech forests?Environmental monitoring and assessment · 2024Article
- Retrospective Study of the Prevalence, Histopathology, Therapy, and Survival Time of Neoplastic Disease in Fish.Animals : an open access journal from MDPI · 2024Article
- A boosting first-hitting-time model for survival analysis in high-dimensional settings.Lifetime data analysis · 2023Article
- Proteomics biomarker discovery for individualized prevention of familial pancreatic cancer using statistical learning.PloS one · 2023Article
- A Multi-Institutional Collaboration to Understand Neoplasia, Treatment and Survival of Snakes.Animals : an open access journal from MDPI · 2022Article
- Predicting Physician Consultations for Low Back Pain Using Claims Data and Population-Based Cohort Data-An Interpretable Machine Learning Approach.International journal of environmental research and public health · 2021Article
- Galaxy-ML: An accessible, reproducible, and scalable machine learning toolkit for biomedicine.PLoS computational biology · 2021Article
- Feature Selection Method Based on Partial Least Squares and Analysis of Traditional Chinese Medicine Data.Computational and mathematical methods in medicine · 2019Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Statistical boosting algorithms have triggered a lot of research during the last decade. They combine a powerful machine learning approach with classical statistical modelling, offering various practical advantages like automated variable selection and implicit regularization of effect estimates. They are extremely flexible, as the underlying base-learners (regression functions defining the type of effect for the explanatory variables) can be combined with any kind of loss function (target function to be optimized, defining the type of regression setting). In this review article, we highlight the most recent methodological developments on statistical boosting regarding variable selection, functional regression, and advanced time-to-event modelling. Additionally, we provide a short overview on relevant applications of statistical boosting in biomedicine.
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.