Development of prediction models of spontaneous ureteral stone passage through machine learning: Comparison with conventional statistical analysis

OBJECTIVES: To develop a prediction model of spontaneous ureteral stone passage (SSP) using machine learning and logistic regression and compare the performance of the two models. Indications for management of ureteral stones are unclear, and the clinician determines whether to wait for SSP or perfo...

Descripción completa

Detalles Bibliográficos
Autores principales: Park, Jee Soo, Kim, Dong Wook, Lee, Dongu, Lee, Taeju, Koo, Kyo Chul, Han, Woong Kyu, Chung, Byung Ha, Lee, Kwang Suk
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2021
Materias:
Research Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8635399/
https://www.ncbi.nlm.nih.gov/pubmed/34851999
http://dx.doi.org/10.1371/journal.pone.0260517

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8635399/
https://www.ncbi.nlm.nih.gov/pubmed/34851999
http://dx.doi.org/10.1371/journal.pone.0260517

Ejemplares similares