Comprehensive Echocardiographic Data Enhances Machine Learning Prediction of Heart Failure Readmission Following Myocardial Infarction - 07/08/25
Doi : 10.1016/j.hlc.2025.06.297
L. Scanlon 1, 2, C. Loke 3, N. Chan 3, M. Mallouhi 3, W. Vollbon 4, P. Stewart 2, 3, J. Atherton 3, 5, A. Lin 1, S. Prasad 2, 3, 5
1 Monash Heart, Victorian Heart Hospital, Melbourne, VIC, Australia
2 Faculty of Medicine, The University of Queensland, Brisbane, QLD, Australia
3 Department of Cardiology, Royal Brisbane and Women’s Hospital, Brisbane, QLD, Australia
4 Statewide Cardiac Network, Ministry of Health, Brisbane, QLD, Australia
5 Faculty of Medicine, Griffith University, Southport, Gold Coast, QLD, Australia
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© 2025
Pubblicato da Elsevier Masson SAS.
Vol 34 - N° S4
P. S283-S284 - agosto 2025 Ritorno al numero
Articolo precedente
- Comparison of Four Novel Biomarkers Alongside NT-proBNP for Risk Assessment of Heart Failure or Death Within 1 Year From Admission With Acute Dyspnoea
- J. Chew-Harris, W. Kuan, I. Ibrahim, S. Chan, C. Frampton, Z. Li, O. Liew, R. Troughton, L. Tan, W. Lin, S. Ooi, C. Pemberton, M. Richards
- Compression Therapy in Heart Failure: A Review of the Evidence
- D. Cheung, K. Robinson, S. Sheppard-Law, P. Sinclair, J. McVeigh
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