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Diabetes Mellitus, Type 2

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Identifying people at risk of developing type 2 diabetes: A comparison of predictive analytics techniques and predictor variables.

International journal of medical informatics
BACKGROUND: The present study aims to identify the patients at risk of type 2 diabetes (T2D). There is a body of literature that uses machine learning classification algorithms to predict development of T2D among patients. The current study compares ...

The involvement of phenolic-rich extracts from Galician autochthonous extra-virgin olive oils against the α-glucosidase and α-amylase inhibition.

Food research international (Ottawa, Ont.)
'Brava' and 'Mansa de Figueiredo' extra-virgin olive oils (EVOOs) are two varieties identified from north-western Spain. A systematic phenolic characterization of the studied oils was undertaken by LC-ESI-IT-MS. In addition, the role of dietary polyp...

Predicting diabetic retinopathy and identifying interpretable biomedical features using machine learning algorithms.

BMC bioinformatics
BACKGROUND: The risk factors of diabetic retinopathy (DR) were investigated extensively in the past studies, but it remains unknown which risk factors were more associated with the DR than others. If we can detect the DR related risk factors more acc...

Improved perfusion pattern score association with type 2 diabetes severity using machine learning pipeline: Pilot study.

Journal of magnetic resonance imaging : JMRI
BACKGROUND: Type 2 diabetes mellitus (T2DM) is associated with alterations in the blood-brain barrier, neuronal damage, and arterial stiffness, thus affecting cerebral metabolism and perfusion. There is a need to implement machine-learning methodolog...

Factors associated with dementia in elderly.

Ciencia & saude coletiva
We analyzed the factors associated with dementia in the elderly attended at a memory outpatient clinic of the University of Southern Santa Catarina (UNISUL). This is a cross-sectional study with data analysis of medical records from January 2013 to A...

Pharmacological therapy selection of type 2 diabetes based on the SWARA and modified MULTIMOORA methods under a fuzzy environment.

Artificial intelligence in medicine
Medication selection for Type 2 Diabetes (T2D) is a challenging medical decision-making problem involving multiple medications that can be prescribed to control the patient's blood glucose. The wide range of hyperglycemia lowering agents with varying...

Personalized prediction of drug efficacy for diabetes treatment via patient-level sequential modeling with neural networks.

Artificial intelligence in medicine
Patients with type 2 diabetes mellitus are generally under continuous long-term medical treatment based on anti-diabetic drugs to achieve the desired glucose level. Thus, each patient is associated with a sequence of multiple records for prescription...

DMTO: a realistic ontology for standard diabetes mellitus treatment.

Journal of biomedical semantics
BACKGROUND: Treatment of type 2 diabetes mellitus (T2DM) is a complex problem. A clinical decision support system (CDSS) based on massive and distributed electronic health record data can facilitate the automation of this process and enhance its accu...

Comparing Three Data Mining Algorithms for Identifying the Associated Risk Factors of Type 2 Diabetes.

Iranian biomedical journal
BACKGROUND: Increasing the prevalence of type 2 diabetes has given rise to a global health burden and a concern among health service providers and health administrators. The current study aimed at developing and comparing some statistical models to i...