AIMC Topic: Diabetes Mellitus, Type 2

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The parameter Houlihan: A solution to high-throughput identifiability indeterminacy for brutally ill-posed problems.

Mathematical biosciences Aug 24, 2019
One way to interject knowledge into clinically impactful forecasting is to use data assimilation, a nonlinear regression that projects data onto a mechanistic physiologic model, instead of a set of functions, such as neural networks. Such regressions...
Blood Glucose Insulin Diabetes Mellitus, Type 2 Models, Statistical Models, Biological Humans Machine Learning Adult Middle Aged
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The virtual doctor: An interactive clinical-decision-support system based on deep learning for non-invasive prediction of diabetes.

Artificial intelligence in medicine Aug 21, 2019
Artificial intelligence (AI) will pave the way to a new era in medicine. However, currently available AI systems do not interact with a patient, e.g., for anamnesis, and thus are only used by the physicians for predictions in diagnosis or prognosis. ...
Probability Waist Circumference Body Height Speech Recognition Software Decision Support Systems, Clinical Diabetes Mellitus, Type 2 Body Mass Index Body Weight Humans Neural Networks, Computer Artificial Intelligence Female Deep Learning Surveys and Questionnaires User-Computer Interface Male Middle Aged
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Machine Learning Models in Type 2 Diabetes Risk Prediction: Results from a Cross-sectional Retrospective Study in Chinese Adults.

Current medical science Jul 25, 2019
Type 2 diabetes mellitus (T2DM) has become a prevalent health problem in China, especially in urban areas. Early prevention strategies are needed to reduce the associated mortality and morbidity. We applied the combination of rules and different mach...
Humans Machine Learning Retrospective Studies Female China Diabetes Mellitus, Type 2 Adult Cross-Sectional Studies Male Risk Assessment
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TyG-er: An ensemble Regression Forest approach for identification of clinical factors related to insulin resistance condition using Electronic Health Records.

Computers in biology and medicine Jul 17, 2019
BACKGROUND: Insulin resistance is an early-stage deterioration of Type 2 diabetes. Identification and quantification of insulin resistance requires specific blood tests; however, the triglyceride-glucose (TyG) index can provide a surrogate assessment...
Humans Machine Learning Electronic Health Records Blood Glucose Diabetes Mellitus, Type 2 Insulin Resistance Triglycerides Female Aged Male Middle Aged
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[Maturity Onset Diabetes of the Young: case report].

Revista medica del Instituto Mexicano del Seguro Social Jul 1, 2019
BACKGROUND: Maturity Onset Diabetes of the Young (MODY) is a type of diabetes that results from mutations in 13 known genes that play a role in the development and maturation of pancreatic beta cells. It represents 5% of all individuals diagnosed wit...
Diabetes Mellitus, Type 2 Humans Glucose Tolerance Test Biomarkers C-Peptide Female Young Adult
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Machine-learning to stratify diabetic patients using novel cardiac biomarkers and integrative genomics.

Cardiovascular diabetology Jun 11, 2019
BACKGROUND: Diabetes mellitus is a chronic disease that impacts an increasing percentage of people each year. Among its comorbidities, diabetics are two to four times more likely to develop cardiovascular diseases. While HbA1c remains the primary dia...
CpG Islands Systems Integration Mitochondria, Heart Diabetic Cardiomyopathies Genetic Predisposition to Disease Polymorphism, Single Nucleotide Epigenesis, Genetic Models, Genetic Glycated Hemoglobin Genetic Markers DNA, Mitochondrial Support Vector Machine DNA Methylation Risk Assessment Humans Prognosis Disease Progression Female Diabetes Mellitus, Type 2 Risk Factors Genomics Male Middle Aged
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Prediction of Nephropathy in Type 2 Diabetes: An Analysis of the ACCORD Trial Applying Machine Learning Techniques.

Clinical and translational science May 31, 2019
Applying data mining and machine learning (ML) techniques to clinical data might identify predictive biomarkers for diabetic nephropathy (DN), a common complication of type 2 diabetes mellitus (T2DM). A retrospective analysis of the Action to Control...
Biomarkers Female Risk Factors Sensitivity and Specificity Incidence ROC Curve Diabetes Mellitus, Type 2 Male Middle Aged Humans Machine Learning Diabetic Nephropathies Models, Theoretical Data Mining
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Towards more Accessible Precision Medicine: Building a more Transferable Machine Learning Model to Support Prognostic Decisions for Micro- and Macrovascular Complications of Type 2 Diabetes Mellitus.

Journal of medical systems May 17, 2019
Although machine learning models are increasingly being developed for clinical decision support for patients with type 2 diabetes, the adoption of these models into clinical practice remains limited. Currently, machine learning (ML) models are being ...
Aged Adult Male Middle Aged Diabetes Complications Machine Learning Decision Support Systems, Clinical Diabetes Mellitus, Type 2 Precision Medicine Humans Prognosis Female
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Predictive Estimates of Risks Associated with Type 2 Diabetes Mellitus on the Basis of Biochemical Biomarkers and Derived Time-Dependent Parameters.

Journal of computational biology : a journal of computational molecular cell biology Apr 17, 2019
Alanine Transaminase Aspartate Aminotransferases Big Data Blood Glucose Diabetes Mellitus, Type 2 Cholesterol Prognosis Female Humans Risk Factors Machine Learning Male Biomarkers Risk Assessment
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Simple Linear Support Vector Machine Classifier Can Distinguish Impaired Glucose Tolerance Versus Type 2 Diabetes Using a Reduced Set of CGM-Based Glycemic Variability Indices.

Journal of diabetes science and technology Mar 31, 2019
BACKGROUND: Many glycemic variability (GV) indices exist in the literature. In previous works, we demonstrated that a set of GV indices, extracted from continuous glucose monitoring (CGM) data, can distinguish between stages of diabetes progression. ...
Blood Glucose Self-Monitoring Datasets as Topic Glucose Intolerance Glycemic Control Support Vector Machine Diagnosis, Differential Predictive Value of Tests Data Interpretation, Statistical Algorithms Female Reproducibility of Results Aged Health Status Indicators Middle Aged Humans Adult Blood Glucose Male Diabetes Mellitus, Type 2
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