Cargando…
Revolutionizing Chronic Kidney Disease Management with Machine Learning and Artificial Intelligence
Autores principales: | Krisanapan, Pajaree, Tangpanithandee, Supawit, Thongprayoon, Charat, Pattharanitima, Pattharawin, Cheungpasitporn, Wisit |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10143586/ https://www.ncbi.nlm.nih.gov/pubmed/37109354 http://dx.doi.org/10.3390/jcm12083018 |
Ejemplares similares
-
Recent Advances in Understanding of Cardiovascular Diseases in Patients with Chronic Kidney Disease
por: Krisanapan, Pajaree, et al.
Publicado: (2022) -
Distinct Subtypes of Hepatorenal Syndrome and Associated Outcomes as Identified by Machine Learning Consensus Clustering
por: Tangpanithandee, Supawit, et al.
Publicado: (2023) -
Explainable Preoperative Automated Machine Learning Prediction Model for Cardiac Surgery-Associated Acute Kidney Injury
por: Thongprayoon, Charat, et al.
Publicado: (2022) -
Clinical Phenotypes of Dual Kidney Transplant Recipients in the United States as Identified through Machine Learning Consensus Clustering
por: Tangpanithandee, Supawit, et al.
Publicado: (2022) -
Differences between Kidney Transplant Recipients from Deceased Donors with Diabetes Mellitus as Identified by Machine Learning Consensus Clustering
por: Thongprayoon, Charat, et al.
Publicado: (2023)