Hypertension

Synthetic, Population-Based Virtual Patient Database Using a Digital Twin of the Cardiovascular System.

PURPOSE: The goal is to develop a cardiovascular virtual patient database (VPD) combining physiological and demographic data to provide the foundation for future applications in medical diagnostics, decision-making, credibility testing, and formal uncertainty analysis, and to enable its integration with three-dimensional (3D) hemodynamic models and to train neural networks. METHODS: We generate an...

Leveraging natural language processing and machine learning to identify chronic conditions from primary care electronic medical records.

Primary care electronic medical records (EMRs) contain rich data that can support proactive identification of chronic health conditions. However, leveraging unstructured EMR data requires the use of novel computational methods. We applied natural language processing and machine learning (ML) techniques to structured and unstructured EMR data to detect arthritis, chronic kidney disease, diabetes, h...

Machine learning-based on model for explain risk of 24-hour death in critically ill patients in the prehospital setting: A retrospective cohort study.

This study aimed to develop and validate a machine learning-based model for predicting 24-hour mortality in critically ill patients using prehospital ...

From Observation to Prediction: Machine Learning Analysis of Progression of Visual loss in Nonarteritic Anterior Ischemic Optic Neuropathy.

OBJECTIVE: To determine whether combinations of modifiable clinical/systemic risk factors and structured trial variables predict early disease progres...

DNA Methylation Markers for Pregnancy Hypertension via Machine Learning Methods.

BACKGROUND: This study aims to develop a prediction model to identify individuals at risk of hypertensive disorders of pregnancy (HDPs), including ges...

Data-driven subgroups for 3-year risk stratification of incident diabetes and complications in diabetes-free Chinese adults.

BACKGROUND: Substantial metabolic heterogeneity exists prior to the development of diabetes, creating opportunities for earlier and more precise inter...

Health-oriented sleep states: making sleep states reflect health conditions.

STUDY OBJECTIVES: The rich information in sleep offers insights into brain function and overall health. The current guidelines for sleep staging by th...

From clinics to the environment: A systematic review of machine learning and MALDI-TOF MS in the identification of antimicrobial-resistant bacteria.

As a result of the increasing prevalence of Antibiotic-resistant bacteria (ARB) and antibiotic-resistant genes (ARGs) in both community and hospital s...

Machine-learning-derived phenotypes of hypertensive patients using multidimensional clinical and echocardiographic data including strain imaging.

AIMS: We applied unsupervised machine learning clustering to a large cohort of hypertensive patients undergoing echocardiography with strain imaging t...

Sex differences in hemodynamics and remodeling patterns uncovered by automated Machine-Learning 3D echocardiography in aortic stenosis with preserved ejection fraction.

Women with aortic stenosis (AS) are underdiagnosed and undertreated compared to men and face a higher mortality risk despite similar symptoms and fewe...

Cardiac MR function analysis with DL-based super resolution reconstruction: application in the clinical setting.

To assess differences in volumetry, image quality and acquisition time between balanced steady-state free precession cine sequences acquired using (a)...

Factors Associated with Machine Learning-Based Predictions of Retinal Aging Using Teleretinal Screening Images from Patients with Diabetes.

PURPOSE: To identify factors associated with accelerated retinal aging based on machine learning predictions of age using fundus images from teleretin...

Development of an explainable machine learning model for predicting depression in adults with type 2 diabetes mellitus: A cross-sectional SHAP-based analysis of NHANES 2009-2023.

Depression (DEP) is a common yet underdiagnosed comorbidity in adults with type 2 diabetes mellitus (T2DM), worsening glycemic control and increasing ...

Prediction of Fontan failure and correlates of Fontan-associated liver disease severity using machine learning and radiomic features from multi-parametric abdominal MRI.

BACKGROUND: Fontan-associated liver disease (FALD) is associated with morbidity and mortality in patients with palliated single ventricle congenital h...

ECG and PPG Signals-Based Premature Ventricular Contraction Detection Methods: A Review, Key Challenges, and Future Directions.

Premature ventricular contraction (PVC) is a common cardiac arrhythmia, and its timely and automated detection is crucial for preventing life-threaten...

Development and multicentre validation of an artificial intelligence electrocardiogram model for ventricular remodeling in repaired tetralogy of Fallot.

AIMS: Periodic cardiac MRI (CMR) is recommended to identify adverse ventricular remodelling in repaired tetralogy of Fallot (TOF), but access to CMR i...

A novel explainable deep-learning approach for network analysis of epistatic interactions.

Epistatic interactions of gene loci often determine complex trait phenotypes and may indicate the underlying molecular mechanisms of traits and diseas...

Prognostic Value of Artificial Intelligence-Enabled Electrocardiography-Derived Diastolic Dysfunction Grading and Trajectory in Patients Undergoing Transcatheter Aortic Valve Replacement.

BACKGROUND: Artificial intelligence (AI)-enabled electrocardiography has emerged as a tool for detecting cardiac dysfunction. The prognostic relevance...

First-Trimester Machine Learning to Predict Preeclampsia in Normotensive Pregnancies by American Heart Association Guidelines.

This study aimed to determine whether unsupervised machine learning can identify phenotypically distinct subgroups at increased risk for preeclampsia ...

Machine learning models for predicting futile recanalisation after endovascular treatment in patients with large core infarction.

BACKGROUND: Predicting futile recanalisation following endovascular treatment (EVT) in patients with large core infarctions is crucial for guiding cli...