AIMC Topic: Diabetes Mellitus, Type 2

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Prediction of Incident Diabetes in the Jackson Heart Study Using High-Dimensional Machine Learning.

PloS one
Statistical models to predict incident diabetes are often based on limited variables. Here we pursued two main goals: 1) investigate the relative performance of a machine learning method such as Random Forests (RF) for detecting incident diabetes in ...

A machine learning-based framework to identify type 2 diabetes through electronic health records.

International journal of medical informatics
OBJECTIVE: To 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 wit...

Temporal changes in plasma markers of oxidative stress following laparoscopic sleeve gastrectomy in subjects with impaired glucose regulation.

Surgery for obesity and related diseases : official journal of the American Society for Bariatric Surgery
BACKGROUND: Laparoscopic sleeve gastrectomy (LSG) is an effective treatment for obesity and associated metabolic complications. Obesity and type 2 diabetes are associated with increased oxidative stress. Previous studies have examined changes in plas...

Diet-induced weight loss and markers of endothelial dysfunction and inflammation in treated patients with type 2 diabetes.

Clinical nutrition ESPEN
BACKGROUND & AIMS: Overweight and obesity increase cardiovascular mortality in patients with type 2 diabetes (T2D). In a recent trial, however, diet-induced weight loss did not reduce the cardiovascular risk of patients with T2D, possibly due to the ...

Learning statistical models of phenotypes using noisy labeled training data.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVE: Traditionally, patient groups with a phenotype are selected through rule-based definitions whose creation and validation are time-consuming. Machine learning approaches to electronic phenotyping are limited by the paucity of labeled traini...

Resting Heart Rate Does Not Predict Cardiovascular and Renal Outcomes in Type 2 Diabetic Patients.

Journal of diabetes research
Elevated resting heart rate (RHR) has been associated with increased risk of mortality and cardiovascular events. Limited data are available so far in type 2 diabetic (T2DM) subjects with no study focusing on progressive renal decline specifically. A...

Reverse Engineering and Evaluation of Prediction Models for Progression to Type 2 Diabetes: An Application of Machine Learning Using Electronic Health Records.

Journal of diabetes science and technology
BACKGROUND: Application of novel machine learning approaches to electronic health record (EHR) data could provide valuable insights into disease processes. We utilized this approach to build predictive models for progression to prediabetes and type 2...