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Diabetes Mellitus, Type 2

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Building Risk Prediction Models for Type 2 Diabetes Using Machine Learning Techniques.

Preventing chronic disease
INTRODUCTION: As one of the most prevalent chronic diseases in the United States, diabetes, especially type 2 diabetes, affects the health of millions of people and puts an enormous financial burden on the US economy. We aimed to develop predictive m...

Predicting the onset of type 2 diabetes using wide and deep learning with electronic health records.

Computer methods and programs in biomedicine
OBJECTIVE: Diabetes is responsible for considerable morbidity, healthcare utilisation and mortality in both developed and developing countries. Currently, methods of treating diabetes are inadequate and costly so prevention becomes an important step ...

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

Mathematical biosciences
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...

The virtual doctor: An interactive clinical-decision-support system based on deep learning for non-invasive prediction of diabetes.

Artificial intelligence in medicine
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. ...

Machine Learning Models in Type 2 Diabetes Risk Prediction: Results from a Cross-sectional Retrospective Study in Chinese Adults.

Current medical science
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...

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
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...

[Maturity Onset Diabetes of the Young: case report].

Revista medica del Instituto Mexicano del Seguro Social
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...

Prediction of Nephropathy in Type 2 Diabetes: An Analysis of the ACCORD Trial Applying Machine Learning Techniques.

Clinical and translational science
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...