| Autor: |
Georgios Feretzakis, Aikaterini Sakagianni, Evangelos Loupelis, Dimitris Kalles, Vasileios Panteris, Lazaros Tzelves, Rea Chatzikyriakou, Nikolaos Trakas, Stavroula Kolokytha, Polyxeni Batiani, Zoi Rakopoulou, Aikaterini Tika, Stavroula Petropoulou, Ilias Dalainas, Vasileios Kaldis |
| Rok vydání: |
2022 |
| DOI: |
10.3233/shti220422 |
| Popis: |
The objective of this study was to evaluate the predictive capability of five machine learning models regarding the admission or discharge of emergency department patients. A Random Forest classifier outperformed other models with respect to the area under the receiver operating characteristic curve (AUC ROC). |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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