Machine learning prediction of stone-free success in patients with urinary stone after treatment of shock wave lithotripsy

BACKGROUND: The aims of this study were to determine the predictive value of decision support analysis for the shock wave lithotripsy (SWL) success rate and to analyze the data obtained from patients who underwent SWL to assess the factors influencing the outcome by using machine learning methods. M...

Descripción completa

Detalles Bibliográficos
Autores principales: Yang, Seung Woo, Hyon, Yun Kyong, Na, Hyun Seok, Jin, Long, Lee, Jae Geun, Park, Jong Mok, Lee, Ji Yong, Shin, Ju Hyun, Lim, Jae Sung, Na, Yong Gil, Jeon, Kiwan, Ha, Taeyoung, Kim, Jinbum, Song, Ki Hak
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2020
Materias:
Research Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7333255/
https://www.ncbi.nlm.nih.gov/pubmed/32620102
http://dx.doi.org/10.1186/s12894-020-00662-x

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7333255/
https://www.ncbi.nlm.nih.gov/pubmed/32620102
http://dx.doi.org/10.1186/s12894-020-00662-x

Ejemplares similares