AIMC Topic: Hypertension

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Health improvement framework for actionable treatment planning using a surrogate Bayesian model.

Nature communications
Clinical decision-making regarding treatments based on personal characteristics leads to effective health improvements. Machine learning (ML) has been the primary concern of diagnosis support according to comprehensive patient information. A prominen...

Classification and Prediction on the Effects of Nutritional Intake on Overweight/Obesity, Dyslipidemia, Hypertension and Type 2 Diabetes Mellitus Using Deep Learning Model: 4-7th Korea National Health and Nutrition Examination Survey.

International journal of environmental research and public health
Few studies have been conducted to classify and predict the influence of nutritional intake on overweight/obesity, dyslipidemia, hypertension and type 2 diabetes mellitus (T2DM) based on deep learning such as deep neural network (DNN). The present st...

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

Diabetes & metabolic syndrome
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 h...

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

Biosensors
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 ...

Artificial Intelligence in Hypertension: Seeing Through a Glass Darkly.

Circulation research
Hypertension remains the largest modifiable cause of mortality worldwide despite the availability of effective medications and sustained research efforts over the past 100 years. Hypertension requires transformative solutions that can help reduce the...

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

Scientific reports
Patients with severe COVID-19 have overwhelmed healthcare systems worldwide. We hypothesized that machine learning (ML) models could be used to predict risks at different stages of management and thereby provide insights into drivers and prognostic m...

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

Scientific reports
To identify the most important parameters associated with cerebral white matter hyperintensities (WMH), in consideration of potential collinearity, we used a data-driven machine-learning approach. We analysed two independent cohorts (KORA and SHIP). ...

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

Journal of medical Internet research
BACKGROUND: The impending scale up of noncommunicable disease screening programs in low- and middle-income countries coupled with limited health resources require that such programs be as accurate as possible at identifying patients at high risk.

Seated Saline Suppression Test Is Comparable With Captopril Challenge Test for the Diagnosis of Primary Aldosteronism: A Prospective Study.

Endocrine practice : official journal of the American College of Endocrinology and the American Association of Clinical Endocrinologists
OBJECTIVE: The saline suppression test (SST) and captopril challenge test (CCT) are commonly used confirmatory tests for primary aldosteronism (PA). Seated SST (SSST) has been reported to be superior to recumbent SST. Whether SSST is better than CCT ...