ArticleJMIR medical informatics2021
Improving Current Glycated Hemoglobin Prediction in Adults: Use of Machine Learning Algorithms With Electronic Health Records.
Article in JMIR medical informatics, 2021. 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
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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.
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.
- Machine Learning Framework for HbA1c Prediction: Data Enrichment, Cost Optimization, and Interpretability Through Stratified Regression and Multi-Stage Feature Selection.Diagnostics (Basel, Switzerland) · 2026Article
- From raw data to actionable insights: preprocessing real-world data for machine learning in diabetes care.Frontiers in digital health · 2026Article
- Optimising test intervals for individuals with type 2 diabetes: A machine learning approach.PloS one · 2025Article
- Article
- Ensemble neural network modelling for stratified HbA1c prediction: Integrating past glucose measurement as a predictor of glycaemic control.Bioinformation · 2025Article
- Article
- Mixed-effects neural network modelling to predict longitudinal trends in fasting plasma glucose.BMC medical research methodology · 2024Article
- Performance of machine learning versus the national early warning score for predicting patient deterioration risk: a single-site study of emergency admissions.BMJ health & care informatics · 2024Article
- Mobile Application and Machine Learning-Driven Scheme for Intelligent Diabetes Progression Analysis and Management Using Multiple Risk Factors.Bioengineering (Basel, Switzerland) · 2024Article
- The Use of Deep Learning and Machine Learning on Longitudinal Electronic Health Records for the Early Detection and Prevention of Diseases: Scoping Review.Journal of medical Internet research · 2024Article
- From prediction to practice: mitigating bias and data shift in machine-learning models for chemotherapy-induced organ dysfunction across unseen cancers.BMJ oncology · 2024Article
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
backgroundPredicting the risk of glycated hemoglobin (HbA
objectiveOur study investigated the performance of predictive models to forecast HbA
methodsThis study employed multiple logistic regression, random forest, support vector machine, and logistic regression models, as well as a deep learning model (multilayer perceptron) to classify patients with normal (<5.7%) and elevated (≥5.7%) levels of HbA
resultsThe machine learning models achieved promising results for predicting current HbA
conclusionsThis study shows that machine learning models can provide promising results for the task of predicting current HbA
Indexed as
Identifiers
What Socratic 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 Socratic graph.