ReviewEthnicity & disease2020
Machine Learning Methods for Precision Medicine Research Designed to Reduce Health Disparities: A Structured Tutorial.
Review in Ethnicity & disease, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 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
11 citing papers in PubMed.
- Recommended guidelines concerning artificial intelligence and health equity: a scoping review.Health affairs scholar · 2026Review
- Machine Learning Models as Early Warning Systems for Neonatal Infection.Clinics in perinatology · 2025Review
- Enhancing Health Research with Machine Learning: Practical Case Studies Using theData science in science · 2025Article
- Is Generative AI Increasing the Risk for Technology-Mediated Trauma Among Vulnerable Populations?Nursing inquiry · 2025Article
- Development and validation of a machine learning model to predict time to renal replacement therapy in patients with chronic kidney disease.BMC nephrology · 2024Article
- Accelerating health disparities research with artificial intelligence.Frontiers in digital health · 2024Article
- Biomarkers of Arginine Methylation in Diabetic Nephropathy: Novel Insights from Bioinformatics Analysis.Diabetes, metabolic syndrome and obesity : targets and therapy · 2024Article
- Technical Report: Machine-Learning Pipeline for Medical Research and Quality-Improvement Initiatives.Cureus · 2023Article
- Machine learning for the prediction of preclinical airway management in injured patients: a registry-based trial.Clinical and experimental emergency medicine · 2022Article
- Phenotyping of ABCA4 Retinopathy by Machine Learning Analysis of Full-Field Electroretinography.Translational vision science & technology · 2022Article
- Use of Machine Learning Approaches in Clinical Epidemiological Research of Diabetes.Current diabetes reports · 2020Review
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
Precision medicine research designed to reduce health disparities often involves studying multi-level datasets to understand how diseases manifest disproportionately in one group over another, and how scarce health care resources can be directed precisely to those most at risk for disease. In this article, we provide a structured tutorial for medical and public health researchers on the application of machine learning methods to conduct precision medicine research designed to reduce health disparities. We review key terms and concepts for understanding machine learning papers, including supervised and unsupervised learning, regularization, cross-validation, bagging, and boosting. Metrics are reviewed for evaluating machine learners and major families of learning approaches, including tree-based learning, deep learning, and ensemble learning. We highlight the advantages and disadvantages of different learning approaches, describe strategies for interpreting "black box" models, and demonstrate the application of common methods in an example dataset with open-source statistical code in R.
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.