ArticleInternational journal of epidemiology2021
Ensemble modelling in descriptive epidemiology: burden of disease estimation.
Article in International journal of epidemiology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
What it found
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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.
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Who cites it
18 citing papers in PubMed.
- Global Trends in Maternal Mortality and Efforts to Improve Maternal Outcomes Through Sociomedical Interventions.Reproductive sciences (Thousand Oaks, Calif.) · 2026Review
- Comparison and validation of machine learning models to predict 5-year fall risk among community-dwelling older adults in China.BMC geriatrics · 2026Article
- Prediction of Cerebrospinal Fluid (CSF) Pressure with Generative Adversarial Network Synthetic Plasma-CSF Biomarker Pairing.Neuroinformatics · 2025Article
- A State-of-the-Science Review of Long-Term Predictions of Climate Change Impacts on Dengue Transmission Risk.Environmental health perspectives · 2025Review
- District-Level Forecast of Achieving Trachoma Elimination as a Public Health Problem By 2030: An Ensemble Modelling Approach.Clinical infectious diseases : an official publication of the Infectious Diseases Society of America · 2024Article
- Using the Super Learner algorithm to predict risk of major adverse cardiovascular events after percutaneous coronary intervention in patients with myocardial infarction.BMC medical research methodology · 2024Article
- Article
- Machine Learning for Automated Classification of Abnormal Lung Sounds Obtained from Public Databases: A Systematic Review.Bioengineering (Basel, Switzerland) · 2023Review
- Prevalence, years lived with disability, and trends in anaemia burden by severity and cause, 1990-2021: findings from the Global Burden of Disease Study 2021.The Lancet. Haematology · 2023Article
- Ensemble machine learning algorithm for predicting acute kidney injury in patients admitted to the neurointensive care unit following brain surgery.Scientific reports · 2023Article
- Article
- Meta-Dynamic Network Modelling for Biochemical Networks.Methods in molecular biology (Clifton, N.J.) · 2023Article
- Prediction of persistent acute kidney injury in postoperative intensive care unit patients using integrated machine learning: a retrospective cohort study.Scientific reports · 2022Article
- Novel Ensemble Model Recommendation Approach for the Detection of Dyslexia.Children (Basel, Switzerland) · 2022Article
- Modelling the ecological niche of naturally occurring anthrax at global and circumpolar extents using an ensemble modelling framework.Transboundary and emerging diseases · 2022Article
- Ranking the importance of demographic, socioeconomic, and underlying health factors on US COVID-19 deaths: A geographical random forest approach.Health & place · 2022Article
- The global burden of chronic hepatitis B virus infection: comparison of country-level prevalence estimates from four research groups.International journal of epidemiology · 2021Article
- Machine Learning to Develop and Internally Validate a Predictive Model for Post-operative Delirium in a Prospective, Observational Clinical Cohort Study of Older Surgical Patients.Journal of general internal medicine · 2021Observational
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Ensemble modelling is a quantitative method that combines information from multiple individual models and has shown great promise in statistical machine learning. Ensemble models have a theoretical claim to being models that make the 'best' predictions possible. Applications of ensemble models to health research have included applying ensemble models like the super learner and random forests to epidemiological prediction tasks. Recently, ensemble methods have been applied successfully in burden of disease estimation. This article aims to provide epidemiologists with a practical understanding of the mechanisms of an ensemble model and insight into constructing ensemble models that are grounded in the epidemiological dynamics of the prediction problem of interest. We summarize the history of ensemble models, present a user-friendly framework for conceptualizing and constructing ensemble models, walk the reader through a tutorial of applying the framework to an application in burden of disease estimation, and discuss further applications.
Indexed as
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31722368What 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.