Metabolic Syndrome

Ambient Only vs. Longitudinal Data-Enhanced AI Documentation: A Pilot Study Quantifying the Value of Historical Clinical Context in Primary Care

Ambient artificial intelligence (AI) clinical documentation tools have gained rapid adoption in healthcare to address physician burnout from documentation burden. However, current implementations primarily rely on real-time audio capture without systematically incorporating longitudinal patient data, potentially limiting documentation completeness for chronic disease management. To compare documen...

Visionary AI: Decoding Systemic Vascular Health and Hypertensive Disorders in Pregnancy Through Retinal Imaging and Artificial Intelligence

Pregnancy orchestrates a rare physiological transformation across vascular, immune, and metabolic systems. When this dynamic balance is disrupted – as in hypertensive disorders of pregnancy – the consequences can be life-threatening, spanning maternal mortality, fetal growth restriction, and elevated long-term cardiovascular risk. Despite clear links to early placental dysfunction and systemic end...

Genetic and Etiological Insights from Automated Lumen Diameter Measurements in Carotid Ultrasounds of the UK Biobank

Carotid ultrasound is routinely used in clinical practice for non-invasive vascular anatomical and functional assessment. In particular, the carotid i...

A Tabular Residual Neural Network for Diabetes Classification and Prediction

Diabetes Mellitus (DM) is a metabolic disorder characterized by hyperglycemia, with type 1 characterized as an autoimmune destruction of pancreatic be...

Machine learning model predicts new-onset lower extremity deep vein thrombosis after pelvic fracture surgery and targeted diagnosis

Postoperative new-onset deep vein thrombosis (PNO-DVT) of the lower extremities represents a prevalent and serious clinical complication following pel...

Machine Learning Prediction of Blood Pressure Control in Patients With Hypertension and Heart Failure Using Longitudinal Clinical Data

To develop and validate machine learning models for predicting Blood Pressure (BP) control status using demographic characteristics and longitudinal B...

Opportunistically Detecting Signs of Hypertension on a Consumer Smartwatch

Hypertension is a silent killer, with over half of affected adults unaware of their condition1,2. This lack of awareness is a major concern, as early ...

Temporal deep learning with clinically engineered biomarkers for the early prediction of type 2 diabetes

Diabetes mellitus remains a major global health burden, causing an estimated 3.4 million deaths in 2024 and highlighting the need for accurate early i...

A proteogenomic atlas of idiopathic pulmonary arterial hypertension reveals sex-dimorphic mechanisms and potential novel therapeutic targets

Current circulating biomarkers for idiopathic pulmonary arterial hypertension (IPAH) lack specificity for preclinical detection and fail to capture th...

Comparing the Treatment and Side Effects of Existing Bariatric Surgery Procedures: An Observational Study

Bariatric surgery is an established treatment for obesity and its associated comorbidities, including diabetes, hypertension, sleep apnea, and hyperch...

Emerging biomaterials and bio-nano interfaces in pulmonary hypertension therapy: transformative strategies for personalized treatment.

Pulmonary hypertension (PH) is still an aggressive and progressive illness with vascular remodeling and right heart failure despite the therapeutic ad...

Development and validation of an interpretable longitudinal preeclampsia risk prediction using machine learning.

Preeclampsia is a pregnancy-specific disease characterized by new onset hypertension after 20 weeks of gestation that affects 2-8% of all pregnancies ...

Exploring the effect of the triglyceride-glucose index on bone metabolism in prepubertal children, a retrospective study: insights from traditional methods and machine-learning-based bone remodeling prediction.

BACKGROUND: Childhood obesity poses a significant risk to bone health, but the impact of insulin resistance (IR) on bone metabolism in prepubertal chi...

Predicting depression severity using machine learning models: Insights from mitochondrial peptides and clinical factors.

Depression presents a significant challenge to global mental health, often intertwined with factors including oxidative stress. Although the precise r...

Application of machine learning algorithms in predicting new onset hypertension: a study based on the China Health and Nutrition Survey.

BACKGROUND: Hypertension is a serious chronic disease that can significantly lead to various cardiovascular diseases, affecting vital organs such as t...

StackAHTPs: An explainable antihypertensive peptides identifier based on heterogeneous features and stacked learning approach.

Hypertension, often known as high blood pressure, is a major concern to millions of individuals globally. Recent studies have demonstrated the signifi...

Analyzing Demographic Grocery Purchase Patterns in Kenyan Supermarkets Through Unsupervised Learning Techniques.

Kenya is experiencing a significant increase in the prevalence of non-communicable diseases (NCDs) such as cardiovascular diseases, hypertension, Type...

Risk prediction of integrated traditional Chinese and western medicine for diabetes retinopathy based on optimized gradient boosting classifier model.

In order to take full advantage of traditional Chinese medicine (TCM) and western medicine, combined with machine learning technology, to study the ri...

Accelerated Patient-Specific Calibration via Differentiable Hemodynamics Simulations

One of the goals of personalized medicine is to tailor diagnostics to individual patients. Diagnostics are performed in practice by measuring quanti...

Machine Learning Reveals the Contribution of Lipoproteins to Liver Triglyceride Content and Inflammation.

CONTEXT: Metabolic dysfunction-associated steatotic liver disease (MASLD) is currently the most common chronic liver disease worldwide and is strongly...