Background The electrocardiogram (ECG) is one of the most common diagnostic tools
available to assess cardiovascular health. The advent of advanced computational …

Quantifying hemodynamic severity in patients with heart failure (HF) is an integral part of
clinical care. A key indicator of hemodynamic severity is the mean Pulmonary Capillary …

Self-supervised learning (SSL) for clinical time series data has received significant attention
in recent literature, since these data are highly rich and provide important information about …

Artificial intelligence (AI) is described as the joint set of data entry, able to receive inputs,
interpret and learn from such feedbacks, and display related and flexible independent …

Clinical time series data are highly rich and provide significant information about a patient's
physiological state. However, these time series can be complex to model, particularly when …

Background The ability to non-invasively measure left atrial pressure would facilitate the
identification of patients at risk of pulmonary congestion and guide proactive heart failure …

Background Despite the poor outcomes related to the presence of pulmonary hypertension,
it often goes undiagnosed in part because of low suspicion and screening tools not being …

Heart failure is a debilitating condition that affects millions of people worldwide and has a
significant impact on their quality of life and mortality rates. An objective assessment of …

Machine learning, and deep learning in particular, has seen brisk growth in biomedical
research; in 2015, PubMed indexed fewer than 500 citations under “deep learning,” while in …

Recent advancements in non-invasive detection of cardiac hemodynamic instability (CHDI)
primarily focus on applying machine learning techniques to a single data modality, eg …