In the recent decade, deep learning, a subset of artificial intelligence and machine learning,
has been used to identify patterns in big healthcare datasets for disease phenotyping, event …

Purpose The advent of clinically adapted machine learning algorithms can solve numerous
problems ranging from disease diagnosis and prognosis to therapy recommendations. This …

Abstract Machine learning (ML) refers to computational algorithms that iteratively improve
their ability to recognize patterns in data. The digitization of our healthcare infrastructure is …

Machine learning has become a key driver of the digital health revolution. That comes with a
fair share of high hopes and hype. We conducted a scoping review on machine learning in …

Cardiovascular diseases refer to diseases that affect the heart and blood arteries. Most
strategies developed to predict ischemic heart disease (IHD) are focused on pain …

Abstract Machine learning (ML) has emerged as a promising tool to enhance suicidal
prediction. However, as many large-sample studies mixed psychiatric and non-psychiatric …

Purpose To develop a predictive machine learning model to identify prognostic factors for
continued opioid prescriptions after arthroscopic meniscus surgery. Methods Patients …

The neurological ICU (neuro ICU) often suffers from significant limitations due to scarce
resource availability for their neurocritical care patients. Neuro ICU patients require frequent …

Background Cardiovascular magnetic resonance (CMR) is an important imaging modality
for the assessment of heart disease; however, limitations of CMR include long exam times …

Despite the growing interest in possible applications of computer science and artificial
intelligence (AI) in the field of neurocritical care (neuro-ICU), widespread clinical …