Cargando…
Cardiovascular Risk Prediction Using Machine Learning in a Large Japanese Cohort
Background: Cardiovascular disease (CVD) screening entails precise event prediction to orient risk stratification, resource allocation, and insurance policy. We used random survival forests (RSF) to identify markers of incident CVD among Japanese adults enrolled in an employer-mandated screening pro...
Autores principales: | Matheson, Matthew B., Kato, Yoko, Baba, Shinichi, Cox, Christopher, Lima, João A.C., Ambale-Venkatesh, Bharath |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
The Japanese Circulation Society
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9726526/ https://www.ncbi.nlm.nih.gov/pubmed/36530840 http://dx.doi.org/10.1253/circrep.CR-22-0101 |
Ejemplares similares
-
Deep learning-based end-to-end automated stenosis classification and localization on catheter coronary angiography
por: Cong, Chao, et al.
Publicado: (2023) -
WHOLE-BODY MRI TO ASSESS SUBCLINICAL CARDIOVASCULAR DISEASE AND FRAILTY DEVELOPMENT
por: Ambale-Venkatesh, Bharath, et al.
Publicado: (2019) -
Prediction of Mortality in hospitalized COVID-19 patients in a statewide health network
por: Ambale-Venkatesh, Bharath, et al.
Publicado: (2021) -
Regional Strain Score as Prognostic Marker of Cardiovascular Events From the Multi-Ethnic Study of Atherosclerosis (MESA)
por: Pezel, Theo, et al.
Publicado: (2022) -
Estimation of aortic pulse wave transit time in cardiovascular magnetic resonance using complex wavelet cross-spectrum analysis
por: Bargiotas, Ioannis, et al.
Publicado: (2015)