Machine Learning Models for Predicting Postoperative Complications and Hospitalization After Percutaneous Nephrolithotomy

Autores de IIS La Fe

• Maria Pilar Bahilo Mateu

  Autor

• Alberto Budía Alba

  Autor

Participantes ajenos a IIS La Fe

• Shalabayeva L.
• Romeu Ferras M.
• Díaz-Carnicero J.
• Vivas-Consuelo D.

Grupos

• Urología

Abstract

Abstract no disponible

Keywords

• Decision trees; Learning systems; Logistic regression; Machine learning; Prediction models; Random forests; Risk assessment; Statistical tests; Complication; Hospitalization stay; Machine learning models; Machine learning techniques; Machine-learning; Multiple machine; PCNL; Percutaneous nephrolithotomy; Postoperative complications; Predictive models; Forecasting