AIMC Topic: Diabetes Mellitus, Type 2

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An outcome model approach to transporting a randomized controlled trial results to a target population.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVE: Participants enrolled into randomized controlled trials (RCTs) often do not reflect real-world populations. Previous research in how best to transport RCT results to target populations has focused on weighting RCT data to look like the tar...

Artificial Intelligence and Deep Learning: The Future of Medicine and Medical Practice.

The Journal of the Association of Physicians of India
Artificial Intelligence (AI) and access to "Big Data" together with the evolving techniques in biotechnology will change the medical practice a big way. Many diseases such as type II diabetes will no longer be considered as a single disease. Many fam...

Machine Learning for the Prediction of New-Onset Diabetes Mellitus during 5-Year Follow-up in Non-Diabetic Patients with Cardiovascular Risks.

Yonsei medical journal
PURPOSE: Many studies have proposed predictive models for type 2 diabetes mellitus (T2DM). However, these predictive models have several limitations, such as user convenience and reproducibility. The purpose of this study was to develop a T2DM predic...

Acalypha Wilkesiana 'Java White': Identification of Some Bioactive Compounds by Gc-Ms and Their Effects on Key Enzymes Linked to Type 2 Diabete.

Acta pharmaceutica (Zagreb, Croatia)
In this study, we identified bioactive compounds from the ethanolic extracts of the leaves, stem bark and root bark of Acalypha wilkesiana through GC-MS analysis and investigated the effects of these extracts on some of the enzymes linked to type 2 d...

The effects of menopausal hormone therapy on proinflammatory cytokines and immunoglobulins in perimenopausal patients with type 2 diabetes mellitus and chronic obstructive pulmonary disease (COPD).

Terapevticheskii arkhiv
AIM: To determine the effects of menopausal hormone therapy dosage on levels of proinflammatory cytokines and immunoglobulins in bodily fluids of patients with type 2 diabetes mellitus (DM) and chronic obstructive pulmonary disease (COPD) during peri...

[Antidiabetic role of high density lipoproteins].

Biomeditsinskaia khimiia
Disturbance in lipid metabolism can be both a cause and a consequence of the development of diabetes mellitus (DM). One of the most informative indicator of lipid metabolism is the ratio of atherogenic and antiatherogenic fractions of lipoproteins an...

Mechanistic machine learning: how data assimilation leverages physiologic knowledge using Bayesian inference to forecast the future, infer the present, and phenotype.

Journal of the American Medical Informatics Association : JAMIA
We introduce data assimilation as a computational method that uses machine learning to combine data with human knowledge in the form of mechanistic models in order to forecast future states, to impute missing data from the past by smoothing, and to i...

Cardiorenal Outcomes in the CANVAS, DECLARE-TIMI 58, and EMPA-REG OUTCOME Trials: A Systematic Review.

Reviews in cardiovascular medicine
In this systematic review, we sought to summarize the 3 recent sodium-glucose cotransporter 2 inhibitor (SGLT2i) trials (Dapagliflozin Effect on CardiovasculAR Events (DECLARE-TIMI 58), Canagliflozin Cardiovascular Assessment Study (CANVAS) Program, ...

Considerations for automated machine learning in clinical metabolic profiling: Altered homocysteine plasma concentration associated with metformin exposure.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
With the maturation of metabolomics science and proliferation of biobanks, clinical metabolic profiling is an increasingly opportunistic frontier for advancing translational clinical research. Automated Machine Learning (AutoML) approaches provide ex...