Metabolic Syndrome

Survey and Evaluation of Hypertension Machine Learning Research.

Background Machine learning (ML) is pervasive in all fields of research, from automating tasks to complex decision-making. However, applications in different specialities are variable and generally limited. Like other conditions, the number of studies employing ML in hypertension research is growing rapidly. In this study, we aimed to survey hypertension research using ML, evaluate the reporting q...

Body composition predicts hypertension using machine learning methods: a cohort study.

We used machine learning methods to investigate if body composition indices predict hypertension. Data from a cohort study was used, and 4663 records were included (2156 were male, 1099 with hypertension, with the age range of 35-70 years old). Body composition analysis was done using bioelectrical impedance analysis (BIA); weight, basal metabolic rate, total and regional fat percentage (FATP), an...

Differential diagnosis of secondary hypertension based on deep learning.

Secondary hypertension is associated with higher risks of target organ damage and cardiovascular and cerebrovascular disease events. Early aetiology i...

Identifying Reasons for Statin Nonuse in Patients With Diabetes Using Deep Learning of Electronic Health Records.

Background Statins are guideline-recommended medications that reduce cardiovascular events in patients with diabetes. Yet, statin use is concerningly ...

A genome-wide association study of childhood adiposity and blood lipids.

The rising prevalence of childhood obesity and dyslipidaemia is a major public health concern due to its association with morbidity and mortality in ...

Electrocardiogram Detection of Pulmonary Hypertension Using Deep Learning.

BACKGROUND: Pulmonary hypertension (PH) is life-threatening, and often diagnosed late in its course. We aimed to evaluate if a deep learning approach ...

Lorcaserin and phentermine exert anti-obesity effects with modulation of the gut microbiota.

Although drugs have been reported to modulate the gut microbiota, the effects of anti-obesity drugs on the gut microbiota remain unclear. Lorcaserin (...

Discovery of drug-omics associations in type 2 diabetes with generative deep-learning models.

The application of multiple omics technologies in biomedical cohorts has the potential to reveal patient-level disease characteristics and individuali...

The potential role of machine learning in modelling advanced chronic liver disease.

The use of artificial intelligence is rapidly increasing in medicine to support clinical decision making mostly through diagnostic and prediction mode...

Efficient targeted learning of heterogeneous treatment effects for multiple subgroups.

In biomedical science, analyzing treatment effect heterogeneity plays an essential role in assisting personalized medicine. The main goals of analyzin...

Classification and Prediction on Hypertension with Blood Pressure Determinants in a Deep Learning Algorithm.

Few studies classified and predicted hypertension using blood pressure (BP)-related determinants in a deep learning algorithm. The objective of this s...

Expressions of serum adiponectin and visfatin in patients with hypertension in cerebrovascular accidents and analysis of risk factors.

OBJECTIVE: To determine the expressions of serum adiponectin and visfatin in patients with hypertension and cerebrovascular accidents and to analyze t...

Analysis of Characteristic Factors of Nursing Safety Incidents in ENT Surgery by Deep Learning-Based Medical Data Association Rules Method.

It is of great significance to explore the characteristic factors of postoperative nursing safety events in patients with otolaryngology surgery and t...

Predictive Analysis of Diabetes-Risk with Class Imbalance.

Diabetes type 2 (T2DM) is a common chronic disease, increasingly leading to many complications and affecting vital organs. Hyperglycemia is the main c...

Machine learning-based prediction of disability risk in geriatric patients with hypertension for different time intervals.

BACKGROUND: The risk of disability in older adults with hypertension is substantially high, and prediction of disability risk is crucial for subsequen...

Evaluating the risk of hypertension in residents in primary care in Shanghai, China with machine learning algorithms.

OBJECTIVE: The prevention of hypertension in primary care requires an effective and suitable hypertension risk assessment model. The aim of this study...

Detecting High-Risk Factors and Early Diagnosis of Diabetes Using Machine Learning Methods.

Diabetes is a chronic disease that can cause several forms of chronic damage to the human body, including heart problems, kidney failure, depression, ...

Development of early prediction model for pregnancy-associated hypertension with graph-based semi-supervised learning.

Clinical guidelines recommend several risk factors to identify women in early pregnancy at high risk of developing pregnancy-associated hypertension. ...

Improved Harris Hawks Optimization with Hybrid Deep Learning Based Heating and Cooling Load Prediction on residential buildings.

In digital era, energy efficient building remains a hot research topic because of increasing concern regarding their environmental impact and energy c...

Development and External Validation of a Machine Learning Model for the Early Prediction of Doses of Harmful Intracranial Pressure in Patients with Severe Traumatic Brain Injury.

Treatment and prevention of elevated intracranial pressure (ICP) is crucial in patients with severe traumatic brain injury (TBI). Elevated ICP is asso...