Machine Learning Models to Predict Kidney Stone Recurrence Using 24 Hour Urine Testing and Electronic Health Record-Derived Features

OBJECTIVE: To assess the accuracy of machine learning models in predicting kidney stone recurrence using variables extracted from the electronic health record (EHR). METHODS: We trained three separate machine learning (ML) models (least absolute shrinkage and selection operator regression [LASSO], r...

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Detalles Bibliográficos
Autores principales: Doyle, Patrick, Gong, Wu, Hsi, Ryan, Kavoussi, Nicholas
Formato: Online Artículo Texto
Lenguaje:English
Publicado: American Journal Experts 2023
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10350114/
https://www.ncbi.nlm.nih.gov/pubmed/37461654
http://dx.doi.org/10.21203/rs.3.rs-3107998/v1

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10350114/
https://www.ncbi.nlm.nih.gov/pubmed/37461654
http://dx.doi.org/10.21203/rs.3.rs-3107998/v1

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