ReviewHealthcare informatics research2019
Artificial Intelligence Applications in Type 2 Diabetes Mellitus Care: Focus on Machine Learning Methods.
Review in Healthcare informatics research, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers, 1 of them a synthesis that pooled it.
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
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Who cites it
31 citing papers in PubMed, 1 synthesis or guideline pooled it, 99 citations in OpenAlex.
- Evaluation of Machine Learning Methods Developed for Prediction of Diabetes Complications: A Systematic Review.Journal of diabetes science and technology · 2023Pooled it
- Artificial intelligence in mobile health applications: A comprehensive review of its role in diabetes care.World journal of methodology · 2026Review
- Efficacy of Novel Drug Delivery Systems for Insulin Administration and Technological Advances in Diabetes Mellitus: Review.AAPS PharmSciTech · 2025Review
- Advancement of artificial intelligence based treatment strategy in type 2 diabetes: A critical update.Journal of pharmaceutical analysis · 2025Review
- Rising tide: the growing global burden and inequalities of early-onset type 2 diabetes among youths aged 15-34 years (1990-2021).Diabetology & metabolic syndrome · 2025Article
- Diabetic Wound Healing: Factors, Mechanisms, and Treatment Strategies Using Herbal Components.Current drug targets · 2025Review
- Predictors of Medical and Dental Clinic Closure by Machine Learning Methods: Cross-Sectional Study Using Empirical Data.Journal of medical Internet research · 2024Article
- Machine-learning algorithms in screening for type 2 diabetes mellitus: Data from Fasa Adults Cohort Study.Endocrinology, diabetes & metabolism · 2024Article
- Benefit-Risk Assessment of ChatGPT Applications in the Field of Diabetes and Metabolic Illnesses: A Qualitative Study.Clinical medicine insights. Endocrinology and diabetes · 2024Article
- Artificial intelligence in diabetes management: Advancements, opportunities, and challenges.Cell reports. Medicine · 2023Review
- Effect of Artificial Intelligence-based Health Education Accurately Linking System (AI-HEALS) for Type 2 diabetes self-management: protocol for a mixed-methods study.BMC public health · 2023Article
- Machine Learning Approach to Drug Treatment Strategy for Diabetes Care.Diabetes & metabolism journal · 2023Review
- Using Machine Learning for the Risk Factors Classification of Glycemic Control in Type 2 Diabetes Mellitus.Healthcare (Basel, Switzerland) · 2023Article
- Transparent machine learning suggests a key driver in the decision to start insulin therapy in individuals with type 2 diabetes.Journal of diabetes · 2023Article
- Machine-Learning-Based Diabetes Mellitus Risk Prediction Using Multi-Layer Neural Network No-Prop Algorithm.Diagnostics (Basel, Switzerland) · 2023Article
- Value of machine learning algorithms for predicting diabetes risk: A subset analysis from a real-world retrospective cohort study.Journal of diabetes investigation · 2023Article
- Article
- Using Artificial Intelligence to Develop a Multivariate Model with a Machine Learning Model to Predict Complications in Mexican Diabetic Patients without Arterial Hypertension (National Nested Case-Control Study): Metformin and Elevated Normal Blood Pressure Are Risk Factors, and Obesity Is Protective.Journal of diabetes research · 2023Article
- Machine learning approach to predict body weight in adults.Frontiers in public health · 2023Article
- Predicting misdiagnosed adult-onset type 1 diabetes using machine learning.Diabetes research and clinical practice · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesThe incidence of type 2 diabetes mellitus has increased significantly in recent years. With the development of artificial intelligence applications in healthcare, they are used for diagnosis, therapeutic decision making, and outcome prediction, especially in type 2 diabetes mellitus. This study aimed to identify the artificial intelligence (AI) applications for type 2 diabetes mellitus care.
methodsThis is a review conducted in 2018. We searched the PubMed, Web of Science, and Embase scientific databases, based on a combination of related mesh terms. The article selection process was based on Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). Finally, 31 articles were selected after inclusion and exclusion criteria were applied. Data gathering was done by using a data extraction form. Data were summarized and reported based on the study objectives.
resultsThe main applications of AI for type 2 diabetes mellitus care were screening and diagnosis in different stages. Among all of the reviewed AI methods, machine learning methods with 71% (n = 22) were the most commonly applied techniques. Many applications were in multi method forms (23%). Among the machine learning algorithms applications, support vector machine (21%) and naive Bayesian (19%) were the most commonly used methods. The most important variables that were used in the selected studies were body mass index, fasting blood sugar, blood pressure, HbA1c, triglycerides, low-density lipoprotein, high-density lipoprotein, and demographic variables.
conclusionsIt is recommended to select optimal algorithms by testing various techniques. Support vector machine and naive Bayesian might achieve better performance than other applications due to the type of variables and targets in diabetes-related outcomes classification.
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.