ArticleAMIA ... Annual Symposium proceedings. AMIA Symposium2023
Improving machine learning with ensemble learning on observational healthcare data.
Article in AMIA ... Annual Symposium proceedings. AMIA Symposium, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 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
27 citing papers in PubMed.
- Prescriptive Predictors of Mindfulness Ecological Momentary Intervention for Social Anxiety Disorder: Machine Learning Analysis of Randomized Controlled Trial Data.JMIR mental health · 2025Trial
- Role prompting modulates linguistic style but not clinical decision structure in GPT-5 tumour board simulation.NPJ digital medicine · 2026Article
- A Class-Wise Deep Ensemble Framework Using ResNet101 and DenseNet201 for Brain Tumor Classification.Journal of imaging informatics in medicine · 2026Article
- Machine Learning Stratification of Periodontal and Cerebrovascular Disease Status Using Salivary and Subgingival Lipopolysaccharide Activity.Journal of cellular and molecular medicine · 2026Article
- Ensemble Machine Learning for Malaria Diagnosis in Resource-Limited Settings Using Clinical and Demographic Features.Infectious disease reports · 2026Article
- Predicting Ketamine Exposure in Pediatric Status Epilepticus: A Pharmacokinetic-Machine Learning Approach.European journal of drug metabolism and pharmacokinetics · 2026Article
- Machine-Learning-Based Survival Prediction in Glioblastoma Using Graph-Theoretical Analysis of Structural Network Alterations.Cancers · 2026Article
- TheraMind: a multi-LLM ensemble for accelerating drug repurposing in lung cancer via case report mining.NPJ precision oncology · 2026Article
- Enhancing protein immunogenicity prediction via uncertainty weighted deep ensemble.Oxford open immunology · 2026Article
- An explainable ensemble machine learning model using baseline blood transcriptomics to predict Parkinson's disease motor progression.Frontiers in digital health · 2026Article
- Article
- Evaluation of large language models in percutaneous coronary intervention decision-making.Frontiers in cardiovascular medicine · 2026Article
- Deep learning-based diagnosis of temporomandibular joint osteoarthritis using whole-body bone scans.iScience · 2025Article
- Predicting Childhood Anaemia in Nigeria: A Machine Learning Approach to Uncover Key Risk Factors.Public health challenges · 2025Article
- Explainable artificial intelligence and ensemble learning for hepatocellular carcinoma classification: State of the art, performance, and clinical implications.World journal of hepatology · 2025Review
- Ensemble learning to enhance accurate identification of patients with glaucoma using electronic health records.JAMIA open · 2025Article
- Advancements in Parkinson's Disease Prediction Using Machine Learning: A Neurological Perspective.Healthcare informatics research · 2025Article
- Machine learning methods, applications and economic analysis to predict heart failure hospitalisation risk: a scoping review.BMJ open · 2025Article
- Stacked Ensemble Learning for Classification of Parkinson's Disease Using Telemonitoring Vocal Features.Diagnostics (Basel, Switzerland) · 2025Article
- Ranking Nursing Diagnoses by Predictive Relevance for Intensive Care Unit Transfer Risk in Adult and Pediatric Patients: A Machine Learning Approach with Random Forest.Healthcare (Basel, Switzerland) · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
Ensemble learning is a powerful technique for improving the accuracy and reliability of prediction models, especially in scenarios where individual models may not perform well. However, combining models with varying accuracies may not always improve the final prediction results, as models with lower accuracies may obscure the results of models with higher accuracies. This paper addresses this issue and answers the question of when an ensemble approach outperforms individual models for prediction. As a result, we propose an ensemble model for predicting patients at risk of postoperative prolonged opioid. The model incorporates two machine learning models that are trained using different covariates, resulting in high precision and recall. Our study, which employs five different machine learning algorithms, shows that the proposed approach significantly improves the final prediction results in terms of AUROC and AUPRC.
Indexed as
Identifiers
38222353PMC10785929What 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.