Abstract Machine learning (ML) is a sub-field of artificial intelligence that uses computer
algorithms to extract patterns from raw data, acquire knowledge without human input, and …

The increasing access to health data worldwide is driving a resurgence in machine learning
research, including data-hungry deep learning algorithms. More computationally efficient …

Background Machine learning (ML) is increasingly used in research for subtype definition
and risk prediction, particularly in cardiovascular diseases. No existing ML models are …

DESIGN: Multimethod, consensus-building approach by members of the Research Agenda
Project Team. DATA SOURCES: Expert opinion, literature review, surveys, interviews, focus …

Heart failure with preserved ejection (HFpEF) is a heterogenous condition affecting nearly
half of all patients with heart failure (HF). Artificial intelligence methodologies can be useful …

Objective Data-driven population segmentation is commonly used in clinical settings to
separate the heterogeneous population into multiple relatively homogenous groups with …

Respect for patient autonomy and data privacy are generally accepted as foundational
western bioethical values. Nonetheless, as our society embraces expanding forms of …

The widespread adoption of mobile technologies offers an opportunity for a new approach to
post-discharge care for patients with heart failure (HF). By enabling non-invasive remote …

This editorial provides an overview of secondary data analysis in nursing science and its
application in a range of contemporary research. The practice of undertaking secondary …

Background Heart failure is currently divided into three main forms, HFrEF, HFpEF, and
HFmrEF, but its etiology is diverse and highly heterogeneous. Many studies reported a …