Metabolic Syndrome

Unsupervised learning using EHR and census data to identify distinct subphenotypes of newly diagnosed hypertension patients.

BACKGROUND: Hypertension (HTN) is a complex condition with significant heterogeneity in presentation and treatment response. Identifying distinct subphenotypes of HTN may improve our understanding of its underlying mechanisms and guide more precise treatment or public health initiatives.

Artificial intelligence and digital twins for the personalised prediction of hypertension risk.

Hypertension is a significant global health challenge, contributing substantially to morbidity and mortality through its association with various cardiovascular diseases. Traditional approaches to hypertension risk prediction, which rely on broad epidemiological data and common risk factors, often fail to account for individual variability, highlighting the need for advanced data-driven methodolog...

Privacy-Preserving Glycemic Management in Type 1 Diabetes: Development and Validation of a Multiobjective Federated Reinforcement Learning Framework.

BACKGROUND: Effective diabetes management requires precise glycemic control to prevent both hypoglycemia and hyperglycemia, yet existing machine learn...

Gut microbiome alterations and hepatic encephalopathy post-TIPS in liver cirrhosis patients.

BACKGROUND: The transjugular intrahepatic portosystemic shunt (TIPS), a crucial tool for treating complications related to portal hypertension in pati...

Network-based machine learning reveals cardiometabolic multimorbidity patterns and modifiable lifestyle factors: a community-focused analysis of NHANES 2015-2018.

Cardiometabolic Multimorbidity (CMM) has emerged as one of the primary threats to human health globally due to its high incidence, disability, and mor...

The value of triglyceride-glucose index-related indices in evaluating migraine: perspectives from multi-centre cross-sectional studies and machine learning models.

BACKGROUND: This study employed representative data from the U.S. and China to delve into the correlation among migraine prevalence, the triglyceride‒...

A model based on artificial intelligence for the prediction, prevention and patient-centred approach for non-communicable diseases related to metabolic syndrome.

Metabolic syndrome (MetS) is related to non-communicable diseases (NCDs) such as type 2 diabetes (T2D), metabolic-associated steatotic liver disease (...

Identifying predictors and assessing causal effect on hypertension risk among adults using Double Machine Learning models: Insights from Bangladesh Demographic and Health Survey.

BACKGROUND: Hypertension poses a significant public health challenge in low- and middle-income countries. In Bangladesh, the Health Population and Nut...

Multimodal nomogram integrating deep learning radiomics and hemodynamic parameters for early prediction of post-craniotomy intracranial hypertension.

To evaluate the effectiveness of deep learning radiomics nomogram in distinguishing early intracranial hypertension (IH) following primary decompressi...

Machine learning and transformer models for prediction of postoperative pneumonia risk in patients with lower limb fractures.

Postoperative pneumonia, a prevalent complication arising from lower limb fracture surgery, can significantly prolong hospitalization periods and elev...

Establishment of a machine learning-based predictive model with dual-center external validation: investigating the role of robotic surgery in preventing delayed gastric emptying for right-sided colon cancer.

After colorectal surgery, delayed gastric emptying (DGE) is a clinically significant postoperative complication that significantly lowers patients' qu...

Identification of individuals who benefit from omega-3 fatty acid supplementation to prevent coronary heart disease: a machine-learning analysis of the VITAL.

Randomized controlled trials (RCTs) have demonstrated benefits of marine omega-3 polyunsaturated fatty acids (omega-3 FA) supplementation for the prev...

In silico prediction of drug-induced cardiotoxicity with ensemble machine learning and structural pattern recognition.

Drug-induced cardiotoxicity poses a significant risk to human health, and reliable predictive models are needed for safety assessment. In this study, ...

Predicting Retinal Nerve Fiber Layer Thickness From Ocular Hypertension Treatment Study Optic Disc Photographs.

IMPORTANCE: Deep learning predictions of retinal nerve fiber layer (RNFL) thickness derived from optic disc photographs may help to determine risk for...

Machine learning prediction of intestinal α-glucosidase inhibitors using a diverse set of ligands: a drug repurposing effort with drugBank database screening.

UNLABELLED: The global rise in diabetes mellitus (DM) poses a significant health challenge, necessitating effective therapeutic interventions. α-Gluco...

Lipidomic profiling of human adiposomes identifies specific lipid shifts linked to obesity and cardiometabolic risk.

BACKGROUNDObesity, a growing health concern, often leads to metabolic disturbances, systemic inflammation, and vascular dysfunction. Emerging evidence...

Prevalence and determinants of hypertension among adults of reproductive age in Tanzania: analysis of a cross-sectional Demographic and Health Survey.

OBJECTIVE: We aimed to estimate prevalence and identify determinants of hypertension in adults aged 15-49 years in Tanzania.

Coarse Grain Preference, Gut Microbiota Features, in Relation to Hypertriglyceridemia in Constipation Patients: A Cross-Sectional Study.

Coarse grain (CG) benefits blood lipids involving gut microbiota regulations. Little is known about the relation between CG preference and blood lipid...

Can we identify individuals at risk to develop multiple myeloma? A machine learning-based predictive model.

Multiple myeloma evolves unnoticed over years, and when diagnosed, organ damage is common. Electronic health records (EHR) can help in developing pred...

The utility of an artificial intelligence model based on decision tree and evolution algorithm to evaluate steatotic liver disease in a primary care setting.

Many ways of classifying steatotic liver disease (SLD) with metabolic conditions have been proposed. Thus, SLD-related variables were verified using a...