A machine learning model incorporating functional assessments predicts one-year mortality in older patients with HF and improves risk stratification beyond established scores. Functional status at ...
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 ...
Abstract: Heart failure remains a major global cause of death and continues to burden healthcare systems. This study proposes an interpretable approach for predicting mortality risk by integrating ...
New biomarkers are needed to improve risk prediction in patients with acute heart failure. We aimed to identify serum lipids with prognostic value and clinical utility in patients hospitalized due to ...
Abstract: Machine learning provides a powerful way of predicting heart failure by identifying hidden patterns using clinical parameters, which is made possible by the abundance of healthcare data.
The final, formatted version of the article will be published soon. Accurate and timely heart disease diagnosis through intelligent ECG signal processing is essential to reducing death rates and ...
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.
Schematic representation of the microbiome study design of 937 CRC patients from the Uppsala-Umeå Comprehensive Cancer Consortium (U-CAN) cohort. A recent study shows that bacteria living inside ...
Objective: Among elderly populations with concurrent type 2 diabetes mellitus (T2DM) and heart failure (HF), 30-day hospital readmission rates range 10%–25% ...
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