AI Medical Compendium Journal:
European heart journal. Digital health

Showing 11 to 16 of 16 articles

Machine learning-derived cycle length variability metrics predict spontaneously terminating ventricular tachycardia in implantable cardioverter defibrillator recipients.

European heart journal. Digital health
AIMS: Implantable cardioverter defibrillator (ICD) therapies have been associated with increased mortality and should be minimized when safe to do so. We hypothesized that machine learning-derived ventricular tachycardia (VT) cycle length (CL) variab...

CURATE.AI-assisted dose titration for anti-hypertensive personalized therapy: study protocol for a multi-arm, randomized, pilot feasibility trial using CURATE.AI (CURATE.AI ADAPT trial).

European heart journal. Digital health
AIMS: Artificial intelligence-driven small data platforms such as CURATE.AI hold potential for personalized hypertension care by assisting physicians in identifying personalized anti-hypertensive doses for titration. This trial aims to assess the fea...

Improving cardiovascular risk prediction through machine learning modelling of irregularly repeated electronic health records.

European heart journal. Digital health
AIMS: Existing electronic health records (EHRs) often consist of abundant but irregular longitudinal measurements of risk factors. In this study, we aim to leverage such data to improve the risk prediction of atherosclerotic cardiovascular disease (A...

Development of a machine learning model using electrocardiogram signals to improve acute pulmonary embolism screening.

European heart journal. Digital health
AIMS: Clinical scoring systems for pulmonary embolism (PE) screening have low specificity and contribute to computed tomography pulmonary angiogram (CTPA) overuse. We assessed whether deep learning models using an existing and routinely collected dat...

Leveraging the potential of machine learning for assessing vascular ageing: state-of-the-art and future research.

European heart journal. Digital health
Vascular ageing biomarkers have been found to be predictive of cardiovascular risk independently of classical risk factors, yet are not widely used in clinical practice. In this review, we present two basic approaches for using machine learning (ML) ...