Metabolic Syndrome

Machine learning algorithm for characterizing risks of hypertension, at an early stage in Bangladesh.

BACKGROUND AND AIMS: Hypertension has become a major public health issue as the prevalence and risk of premature death and disability among adults due to hypertension has increased globally. The main objective is to characterize the risk factors of hypertension among adults in Bangladesh using machine learning (ML) algorithms.

Using CNN and HHT to Predict Blood Pressure Level Based on Photoplethysmography and Its Derivatives.

According to the WTO, there were 1.13 billion hypertension patients worldwide in 2015. The WTO encouraged people to check the blood pressure regularly because a large amount of patients do not have any symptoms. However, traditional cuff measurement results are not enough to represent the patient's blood pressure status over a period of time. Therefore, there is an urgent need for portable, easy t...

Artificial Intelligence in Hypertension: Seeing Through a Glass Darkly.

Hypertension remains the largest modifiable cause of mortality worldwide despite the availability of effective medications and sustained research effo...

Blood Pressure Morphology Assessment from Photoplethysmogram and Demographic Information Using Deep Learning with Attention Mechanism.

Arterial blood pressure (ABP) is an important vital sign from which it can be extracted valuable information about the subject's health. After studyin...

Assessment of Thoracic Pain Using Machine Learning: A Case Study from Baja California, Mexico.

Thoracic pain is a shared symptom among gastrointestinal diseases, muscle pain, emotional disorders, and the most deadly: Cardiovascular diseases. Due...

Developing and validating COVID-19 adverse outcome risk prediction models from a bi-national European cohort of 5594 patients.

Patients with severe COVID-19 have overwhelmed healthcare systems worldwide. We hypothesized that machine learning (ML) models could be used to predic...

Risk factors analysis of COVID-19 patients with ARDS and prediction based on machine learning.

COVID-19 is a newly emerging infectious disease, which is generally susceptible to human beings and has caused huge losses to people's health. Acute r...

Associated factors of white matter hyperintensity volume: a machine-learning approach.

To identify the most important parameters associated with cerebral white matter hyperintensities (WMH), in consideration of potential collinearity, we...

Prediction of death status on the course of treatment in SARS-COV-2 patients with deep learning and machine learning methods.

BACKGROUND AND OBJECTIVE: The new type of Coronavirus (2019-nCov) epidemic spread rapidly, causing more than 250 thousand deaths worldwide. The virus,...

Risk Stratification for Early Detection of Diabetes and Hypertension in Resource-Limited Settings: Machine Learning Analysis.

BACKGROUND: The impending scale up of noncommunicable disease screening programs in low- and middle-income countries coupled with limited health resou...

Towards a new model and classification of mood disorders based on risk resilience, neuro-affective toxicity, staging, and phenome features using the nomothetic network psychiatry approach.

Current diagnoses of mood disorders are not cross validated. The aim of the current paper is to explain how machine learning techniques can be used to...

Network machine learning maps phytochemically rich "Hyperfoods" to fight COVID-19.

In this paper, we introduce a network machine learning method to identify potential bioactive anti-COVID-19 molecules in foods based on their capacity...

Predicting mortality in critically ill patients with diabetes using machine learning and clinical notes.

BACKGROUND: Diabetes mellitus is a prevalent metabolic disease characterized by chronic hyperglycemia. The avalanche of healthcare data is acceleratin...

Predictive value of triglyceride/high-density lipoprotein cholesterol for major clinical outcomes in advanced chronic kidney disease: a nationwide population-based study.

BACKGROUND: Dyslipidemia is an essential parameter in the prediction of cardiovascular disease (CVD). We aimed to explore whether lipid profiles could...

Artificial neural networks for simultaneously predicting the risk of multiple co-occurring symptoms among patients with cancer.

Patients with cancer often exhibit multiple co-occurring symptoms which can impact the type of treatment received, recovery, and long-term health. We ...

Hemodynamic Characteristics and Outcomes of Pulmonary Hypertension in Patients Undergoing Tricuspid Valve Repair or Replacement.

BACKGROUND: The impact of pulmonary hypertension (PH) on outcomes after surgical tricuspid valve replacement (TVR) and repair (TVr) is unclear. We sou...

Machine Learning-Based Risk Assessment for Cancer Therapy-Related Cardiac Dysfunction in 4300 Longitudinal Oncology Patients.

Background The growing awareness of cardiovascular toxicity from cancer therapies has led to the emerging field of cardio-oncology, which centers on p...

Deep learning to predict elevated pulmonary artery pressure in patients with suspected pulmonary hypertension using standard chest X ray.

Accurate diagnosis of pulmonary hypertension (PH) is crucial to ensure that patients receive timely treatment. We hypothesized that application of art...

Type IV Collagen 7S Is the Most Accurate Test For Identifying Advanced Fibrosis in NAFLD With Type 2 Diabetes.

This study aimed to examine whether the diagnostic accuracy of four noninvasive tests (NITs) for detecting advanced fibrosis in nonalcoholic fatty liv...

Effect of Chronotherapy of Antihypertensives in Chronic Kidney Disease: A Randomized Control Trial.

INTRODUCTION: There is a higher prevalence of non-dipping pattern in hypertensive chronic kidney disease (CKD) patients. Nocturnal hypertension has be...