Current models of mortality risk after heart failure (HF) rely primarily on cardiac-specific clinical variables and may underestimate risk in elderly East Asian patients. Researchers from Japan used ...

This module contains the Python code (version 1.0) of the manuscript: Machine learning for smell: Ordinal odor strength prediction of molecular perfumery components (DOI coming soon). It implements ...

Objectives Earlier heart failure (HF) diagnosis in the community could allow timely treatment initiation and prevent unnecessary hospitalisation, but identifying those at risk remains challenging. We ...

Background Machine Learning (ML) has been transformative in healthcare, enabling more precise diagnostics, personalised treatment regimens and enhanced patient care. In cardiology, ML plays a crucial ...

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The PlayStation 4 version will be a digital-only release.

Aaron M Wolfson, MD, and Sarah J Goodlin, MD, discuss nonpharmacologic interventions for patients with heart failure. Despite significant advances in the pharmacologic management of individuals with ...

Chase Markel, a University of Wyoming Ph.D. student from Wheatland, is harnessing artificial intelligence to transform how animal scientists study risk factors for congestive heart failure in cattle.

A patient fact sheet outlining the differences between heart failure and cardiomyopathy, including subtypes of each condition and diagnostic procedures for both. Cardiomyopathy is a group of heart ...

A new global study suggests that a mismatch between two routine blood tests used to assess kidney function may quietly signal elevated risks of kidney failure, heart disease, and death. Credit: ...

Division of Cardiovascular Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN. (B.E.-S., J.L., A.L., B.R.L.) ...

Multimodal Data–Driven Explainable Prognostic Model for Major Adverse Cardiovascular Events Prediction in Patients With Unstable Angina and Heart Failure With Preserved Ejection Fraction: Multicenter, ...