Validating Emergency Department Admission Predictions Based on Local Data Through MIMIC-IV

The effective management of Emergency Department (ED) overcrowding is essential for improving patient outcomes and optimizing healthcare resource allocation. This study validates hospital admission prediction models initially developed using a small local dataset from a Greek hospital by leveraging the comprehensive MIMIC-IV dataset. After preprocessing the MIMIC-IV data, five algorithms were evaluated: Linear Discriminant Analysis (LDA), K-Nearest Neighbors (KNN), Random Forest (RF), Recursive Partitioning and Regression Trees (RPART), and Support Vector Machines (SVM Radial). Among these, RF demonstrated superior performance, achieving an Area Under the Receiver Operating Characteristic Curve (AUC-ROC) of 0.9999, sensitivity of 0.9997, and specificity of 0.9999 when applied to the MIMIC-IV data. These findings highlight the robustness of RF in handling complex datasets for admission prediction, establish MIMIC-IV as a valuable benchmark for validating models based on smaller local datasets, and provide actionable insights for improving ED management strategies.

@article{meimeti2025_2503.22706,
  title={ Validating Emergency Department Admission Predictions Based on Local Data Through MIMIC-IV },
  author={ Francesca Meimeti and Loukas Triantafyllopoulos and Aikaterini Sakagianni and Vasileios Kaldis and Lazaros Tzelves and Nikolaos Theodorakis and Evgenia Paxinou and Georgios Feretzakis and Dimitris Kalles and Vassilios S. Verykios },
  journal={arXiv preprint arXiv:2503.22706},
  year={ 2025 }
}