Cargando…
Predicting in-hospital all-cause mortality in heart failure using machine learning
BACKGROUND: The age of onset and causes of heart failure differ between high-income and low-and-middle-income countries (LMIC). Heart failure patients in LMIC also experience a higher mortality rate. Innovative ways that can risk stratify heart failure patients in this region are needed. The aim of...
Autores principales: | Mpanya, Dineo, Celik, Turgay, Klug, Eric, Ntsinjana, Hopewell |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9875063/ https://www.ncbi.nlm.nih.gov/pubmed/36712268 http://dx.doi.org/10.3389/fcvm.2022.1032524 |
Ejemplares similares
-
Predicting mortality and hospitalization in heart failure using machine learning: A systematic literature review
por: Mpanya, Dineo, et al.
Publicado: (2021) -
Predictive value of remnant cholesterol level for all-cause mortality in heart failure patients
por: Zhao, Lang, et al.
Publicado: (2023) -
Machine learning-based in-hospital mortality risk prediction tool for intensive care unit patients with heart failure
por: Chen, Zijun, et al.
Publicado: (2023) -
Long-Term Visit-to-Visit Mean Arterial Pressure Variability and the Risk of Heart Failure and All-Cause Mortality
por: Liu, Menghui, et al.
Publicado: (2021) -
Native T1 is predictive of cardiovascular death/heart failure events and all-cause mortality irrespective of the patient’s volume status
por: Treiber, Julia, et al.
Publicado: (2023)