ArticleInternational journal of medical informatics2017
A machine learning-based framework to identify type 2 diabetes through electronic health records.
Article in International journal of medical informatics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 81 papers, 5 of them syntheses that pooled it.
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Who cites it
81 citing papers in PubMed, 5 syntheses or guidelines pooled it.
- Machine learning models for identifying prediabetes: a systematic review of performance, validation, and implications for primary care.Frontiers in medicine · 2026Pooled it
- Machine learning and artificial intelligence in type 2 diabetes prediction: a comprehensive 33-year bibliometric and literature analysis.Frontiers in digital health · 2025Pooled it
- Improving Clinical Documentation with Artificial Intelligence: A Systematic Review.Perspectives in health information management · 2024Pooled it
- Accuracy of Machine Learning Classification Models for the Prediction of Type 2 Diabetes Mellitus: A Systematic Survey and Meta-Analysis Approach.International journal of environmental research and public health · 2022Pooled it
- Comparison of Diagnosis Accuracy between a Backpropagation Artificial Neural Network Model and Linear Regression in Digestive Disease Patients: an Empirical Research.Computational and mathematical methods in medicine · 2021Pooled it
- Machine Learning to Identify Point-of-Care Ultrasound and Evaluate Standardized Documentation: Retrospective Operational Cohort Study.Journal of medical Internet research · 2026Article
- Exploring patient motivations and preferences for medical data sharing with researchers: a simulation study using the iAgree platform.Journal of the American Medical Informatics Association : JAMIA · 2026Article
- Machine Learning Driven Insights into Socio-Economic, Demographic, and Health Determinants of Type-II Diabetes: A Tuned Hyperparameter Random Forest Model Using NFHS-5 Data.Journal of pharmacy & bioallied sciences · 2026Article
- Machine Learning for Early Detection and Prevention of Disease Using Electronic Health Records.Methods in molecular biology (Clifton, N.J.) · 2026Review
- Machine learning models for predicting new-onset diabetes following acute pancreatitis using real-world data.BMJ public health · 2026Article
- Development and External Validation of an Algorithm for Identifying HDV RNA-Positive Patients.Gastro hep advances · 2026Article
- Effects of drying processes on the chemical and physical properties of safflower: Towards a multidimensional quality evaluation model.PloS one · 2026Article
- Leveraging Hybrid Natural Language Processing Techniques for Large-Scale Pulmonary Embolism Identification.JACC. Advances · 2025Article
- Using unsupervised machine learning methods to cluster cardio-metabolic profile of the middle-aged and elderly Chinese with general and central obesity.BMC cardiovascular disorders · 2025Article
- Efficient diagnosis of diabetes mellitus using an improved ensemble method.Scientific reports · 2025Article
- Artificial Intelligence in Medicine and Imaging Applications.Current pharmaceutical design · 2025Review
- Article
- Enhancing severe hypoglycemia prediction in type 2 diabetes mellitus through multi-view co-training machine learning model for imbalanced dataset.Scientific reports · 2024Article
- Applying machine learning approaches for predicting obesity risk using US health administrative claims database.BMJ open diabetes research & care · 2024Observational
- Machine learning to identify chronic cough from administrative claims data.Scientific reports · 2024Observational
21 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
objectiveTo discover diverse genotype-phenotype associations affiliated with Type 2 Diabetes Mellitus (T2DM) via genome-wide association study (GWAS) and phenome-wide association study (PheWAS), more cases (T2DM subjects) and controls (subjects without T2DM) are required to be identified (e.g., via Electronic Health Records (EHR)). However, existing expert based identification algorithms often suffer in a low recall rate and could miss a large number of valuable samples under conservative filtering standards. The goal of this work is to develop a semi-automated framework based on machine learning as a pilot study to liberalize filtering criteria to improve recall rate with a keeping of low false positive rate. MATERIALS AND
methodsWe propose a data informed framework for identifying subjects with and without T2DM from EHR via feature engineering and machine learning. We evaluate and contrast the identification performance of widely-used machine learning models within our framework, including k-Nearest-Neighbors, Naïve Bayes, Decision Tree, Random Forest, Support Vector Machine and Logistic Regression. Our framework was conducted on 300 patient samples (161 cases, 60 controls and 79 unconfirmed subjects), randomly selected from 23,281 diabetes related cohort retrieved from a regional distributed EHR repository ranging from 2012 to 2014.
resultsWe apply top-performing machine learning algorithms on the engineered features. We benchmark and contrast the accuracy, precision, AUC, sensitivity and specificity of classification models against the state-of-the-art expert algorithm for identification of T2DM subjects. Our results indicate that the framework achieved high identification performances (∼0.98 in average AUC), which are much higher than the state-of-the-art algorithm (0.71 in AUC). DISCUSSION: Expert algorithm-based identification of T2DM subjects from EHR is often hampered by the high missing rates due to their conservative selection criteria. Our framework leverages machine learning and feature engineering to loosen such selection criteria to achieve a high identification rate of cases and controls.
conclusionsOur proposed framework demonstrates a more accurate and efficient approach for identifying subjects with and without T2DM from EHR.
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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.