Cargando…
County-level phenomapping to identify disparities in cardiovascular outcomes: An unsupervised clustering analysis: Short title: Unsupervised clustering of counties and risk of cardiovascular mortality
INTRODUCTION: Significant heterogeneity in cardiovascular disease (CVD) risk and healthcare resource allocation has been demonstrated in the United States, but optimal methods to capture heterogeneity in county-level characteristics that contribute to CVD mortality differences are unclear. We evalua...
Autores principales: | Segar, Matthew W., Rao, Shreya, Navar, Ann Marie, Michos, Erin D., Lewis, Alana, Correa, Adolfo, Sims, Mario, Khera, Amit, Hughes, Amy E., Pandey, Ambarish |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2020
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8315381/ https://www.ncbi.nlm.nih.gov/pubmed/34327478 http://dx.doi.org/10.1016/j.ajpc.2020.100118 |
Ejemplares similares
-
Phenomapping of Patients with Primary Breast Cancer Using Machine Learning-Based Unsupervised Cluster Analysis
por: Ferro, Sara, et al.
Publicado: (2021) -
Longitudinal Trajectories and Factors Associated With US County-Level Cardiovascular Mortality, 1980 to 2014
por: Rao, Shreya, et al.
Publicado: (2021) -
An unsupervised neuromorphic clustering algorithm
por: Diamond, Alan, et al.
Publicado: (2019) -
Unsupervised learning for county-level typological classification for COVID-19 research
por: Lai, Yuan, et al.
Publicado: (2020) -
GibbsCluster: unsupervised clustering and alignment of peptide sequences
por: Andreatta, Massimo, et al.
Publicado: (2017)