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Diabetes Mellitus, Type 2

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Selecting Test Cases from the Electronic Health Record for Software Testing of Knowledge-Based Clinical Decision Support Systems.

AMIA ... Annual Symposium proceedings. AMIA Symposium
Software testing of knowledge-based clinical decision support systems is challenging, labor intensive, and expensive; yet, testing is necessary since clinical applications have heightened consequences. Thoughtful test case selection improves testing ...

Scalable Electronic Phenotyping For Studying Patient Comorbidities.

AMIA ... Annual Symposium proceedings. AMIA Symposium
Over 75 million Americans have multiple concurrent chronic conditions and medical decision making for these patients is mostly based on retrospective cohort studies. Current methods to generate cohorts of patients with comorbidities are neither scala...

Phenotyping through Semi-Supervised Tensor Factorization (PSST).

AMIA ... Annual Symposium proceedings. AMIA Symposium
A computational phenotype is a set of clinically relevant and interesting characteristics that describe patients with a given condition. Various machine learning methods have been proposed to derive phenotypes in an automatic, high-throughput manner....

Application of Machine Learning Methods to Predict Non-Alcoholic Steatohepatitis (NASH) in Non-Alcoholic Fatty Liver (NAFL) Patients.

AMIA ... Annual Symposium proceedings. AMIA Symposium
Non-alcoholic fatty liver disease (NAFLD) is the leading cause of chronic liver disease worldwide. NAFLD patients have excessive liver fat (steatosis), without other liver diseases and without excessive alcohol consumption. NAFLD consists of a spectr...

Assessing urinary levels of IL-18, NGAL and albumin creatinine ratio in patients with diabetic nephropathy.

Diabetes & metabolic syndrome
AIMS: Diabetic nephropathy (DN) is a serious microvascular complication of a longstanding hyperglycemia. This study aims to evaluate whether urinary neutrophil gelatinase-associated lipocalin (NGAL) and urinary Interleukin-18 possess a better diagnos...

The association between serum uric acid to creatinine ratio and renal disease progression in type 2 diabetic patients in Chinese communities.

Journal of diabetes and its complications
AIMS: Serum uric acid (UA) increases in patients with kidney disease due to the impaired UA clearance. The present study sought to evaluate the association between UA/creatinine ratio (UA/Cr) and renal disease progression in patients with type 2 diab...

Circulating betatrophin in relation to metabolic, inflammatory parameters, and oxidative stress in patients with type 2 diabetes mellitus.

Diabetes & metabolic syndrome
AIMS: Recently, it was suggested that betatrophin has a role in controlling pancreatic β cell proliferation and lipid metabolism, however, its role in human subjects has not been established yet. The predicting role of betatrophin and MDA along with ...

Stacked classifiers for individualized prediction of glycemic control following initiation of metformin therapy in type 2 diabetes.

Computers in biology and medicine
OBJECTIVE: Metformin is the preferred first-line medication for management of type 2 diabetes and prediabetes. However, over a third of patients experience primary or secondary therapeutic failure. We developed machine learning models to predict whic...

Digital Diabetes Data and Artificial Intelligence: A Time for Humility Not Hubris.

Journal of diabetes science and technology
In the future artificial intelligence (AI) will have the potential to improve outcomes diabetes care. With the creation of new sensors for physiological monitoring sensors and the introduction of smart insulin pens, novel data relationships based on ...

Mixed effect machine learning: A framework for predicting longitudinal change in hemoglobin A1c.

Journal of biomedical informatics
Accurate and reliable prediction of clinical progression over time has the potential to improve the outcomes of chronic disease. The classical approach to analyzing longitudinal data is to use (generalized) linear mixed-effect models (GLMM). However,...