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Improving Current Glycated Hemoglobin Prediction in Adults: Use of Machine Learning Algorithms With Electronic Health Records
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Outgoing claims: the papers this work cites, builds on, or argues with.
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Diabetes risk score: towards earlier detection of Type 2 diabetes in general practice
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The Nature of Statistical Learning Theory
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Type-2 Diabetes Mellitus Diagnosis from Time Series Clinical Data Using Deep Learning Models
inferredcites →https://doi.org/10.1016/j.amepre.2010.09.022openalex-snapshot
Identifying Adults at High Risk for Diabetes and Cardiovascular Disease Using Hemoglobin A1c
inferredcites →https://doi.org/10.1016/j.amjmed.2007.03.022openalex-snapshot
Hemoglobin A1c Predicts Diabetes but Not Cardiovascular Disease in Nondiabetic Women
inferredcites →https://doi.org/10.1016/j.cmpb.2017.09.009openalex-snapshot
Development and validation of various phenotyping algorithms for Diabetes Mellitus using data from electronic health records
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Global and regional estimates and projections of diabetes-related health expenditure: Results from the International Diabetes Federation Diabetes Atlas, 9th edition
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Learning from heterogeneous temporal data in electronic health records
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Prognostic Modeling and Prevention of Diabetes Using Machine Learning Technique
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Deep Patient: An Unsupervised Representation to Predict the Future of Patients from the Electronic Health Records
inferredcites →https://doi.org/10.1056/nejm199010113231503openalex-snapshot
Effect of Long-Term Monitoring of Glycosylated Hemoglobin Levels in Insulin-Dependent Diabetes Mellitus
inferredcites →https://doi.org/10.1109/78.650093openalex-snapshot
Bidirectional recurrent neural networks
inferredcites →https://doi.org/10.1109/access.2019.2928363openalex-snapshot
Deep Learning for Electronic Health Records Analytics
inferredcites →https://doi.org/10.1109/icmla.2018.00087openalex-snapshot
Stacked Denoising Autoencoders for Mortality Risk Prediction Using Imbalanced Clinical Data
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The Nature Of Statistical Learning Theory~
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A Review on Ensembles for the Class Imbalance Problem: Bagging-, Boosting-, and Hybrid-Based Approaches
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Data preprocessing issues for incomplete medical datasets
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Electronic health records: new opportunities for clinical research
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Tight blood pressure control and risk of macrovascular and microvascular complications in type 2 diabetes: UKPDS 38
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Association of glycaemia with macrovascular and microvascular complications of type 2 diabetes (UKPDS 35): prospective observational study
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Predicting risk of type 2 diabetes in England and Wales: prospective derivation and validation of QDScore
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Interpretable Machine Learning in Healthcare
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Locally adaptive dimensionality reduction for indexing large time series databases
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Understanding logistic regression analysis
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Interpreting the concordance statistic of a logistic regression model: relation to the variance and odds ratio of a continuous explanatory variable
inferredcites →https://doi.org/10.1186/s12938-018-0604-3openalex-snapshot
Imbalanced biomedical data classification using self-adaptive multilayer ELM combined with dynamic GAN
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Predicting glycated hemoglobin levels in the non-diabetic general population: Development and validation of the DIRECT-DETECT prediction model - a DIRECT study
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SMOTE: Synthetic Minority Over-sampling Technique
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Accuracy of hemoglobin A1c imputation using fasting plasma glucose in diabetes research using electronic health records data
inferredcites →https://doi.org/10.19139/soic.v2i2.68openalex-snapshot
Accuracy of hemoglobin A1c imputation using fasting plasma glucose in diabetes research using electronic health records data
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Machine Learning for Detecting Gene-Gene Interactions
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Diabetes Mellitus in Saudi Arabia: A Review of the Recent Literature
inferredcites →https://doi.org/10.2196/10780openalex-snapshot
Predicting Current Glycated Hemoglobin Values in Adults: Development of an Algorithm From the Electronic Health Record
inferredcites →https://doi.org/10.2196/18963openalex-snapshot
Predicting Current Glycated Hemoglobin Levels in Adults From Electronic Health Records: Validation of Multiple Logistic Regression Algorithm
inferredcites →https://doi.org/10.2337/dc08-0545openalex-snapshot
Translating the A1C Assay Into Estimated Average Glucose Values
inferredcites →https://doi.org/10.2337/dc18-s002openalex-snapshot
2. Classification and Diagnosis of Diabetes: Standards of Medical Care in Diabetes—2018
inferredcites →https://doi.org/10.2337/diacare.22.2.213openalex-snapshot
Performance of a predictive model to identify undiagnosed diabetes in a health care setting.
inferredcites →https://doi.org/10.2337/diacare.29.02.06.dc05-1594openalex-snapshot
Mean Blood Glucose and Biological Variation Have Greater Influence on HbA1c Levels Than Glucose Instability
inferredcites →https://doi.org/10.4103/0256-4947.75773openalex-snapshot
Prevalence of Diabetes Mellitus in a Saudicommunity
inferredcites →https://doi.org/10.5334/aogh.2467openalex-snapshot
Prevalence of Prediabetes, Diabetes and Its Predictors among Females in Alkharj, Saudi Arabia: A Cross-Sectional Study
inferredcites →https://doi.org/10.7326/0003-4819-141-6-200409210-00006openalex-snapshot
Association of Hemoglobin A 1c with Cardiovascular Disease and Mortality in Adults: The European Prospective Investigation into Cancer in Norfolk
inferredcites →https://doi.org/10.7759/cureus.1347openalex-snapshot
Correlation between Glycated Hemoglobin and Triglyceride Level in Type 2 Diabetes Mellitus
inferredcites →https://doi.org/10.7763/ijmlc.2013.v3.307openalex-snapshot
Addressing the Class Imbalance Problem in Medical Datasets
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Incoming claims: where this work appears in other papers’ reference lists and arguments.
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inferred← citeshttps://doi.org/10.1186/s12874-024-02442-9openalex-snapshot
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inferred← citeshttps://doi.org/10.1371/journal.pone.0317722openalex-snapshot
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Predicting Type 2 diabetes onset age using machine learning: A case study in KSA
inferred← citeshttps://doi.org/10.1371/journal.pone.0326315openalex-snapshot
A hybrid approach to enhance HbA1c prediction accuracy while minimizing the number of associated predictors: A case-control study in Saudi Arabia
inferred← citeshttps://doi.org/10.2196/48320openalex-snapshot
The Use of Deep Learning and Machine Learning on Longitudinal Electronic Health Records for the Early Detection and Prevention of Diseases: Scoping Review
inferred← citeshttps://doi.org/10.2196/66831openalex-snapshot
Applications of AI in Predicting Drug Responses for Type 2 Diabetes
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Applications of Artificial Intelligence in predicting drug responses for Type 2 Diabetes (Preprint)
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Normalization Is a Model-Level Design Choice in Outpatient Type 2 Diabetes AI: A Leakage-Safe Comparative Study on Public Datasets (Preprint)
inferred← citeshttps://doi.org/10.3389/fdgth.2026.1685842openalex-snapshot
From raw data to actionable insights: preprocessing real-world data for machine learning in diabetes care
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Mobile Application and Machine Learning-Driven Scheme for Intelligent Diabetes Progression Analysis and Management Using Multiple Risk Factors
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Machine Learning Framework for HbA1c Prediction: Data Enrichment, Cost Optimization, and Interpretability Through Stratified Regression and Multi-Stage Feature Selection
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Classificação do Status Glicêmico a partir de Hemogramas: Avaliação de Estratégias de Aprendizado de Máquina em Dados Laboratoriais Reais do Brasil
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