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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...
| Autores principales: | , , , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
American Journal Experts
2023
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| Materias: | |
| 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 |