<ul><li>This study validates hospital admission prediction models initially developed using a small local dataset from a Greek hospital.</li><li>Using the comprehensive MIMIC-IV dataset, five algorithms were evaluated for prediction: Linear Discriminant Analysis (LDA), K-Nearest Neighbors (KNN), Random Forest (RF), Recursive Partitioning and Regression Trees (RPART), and Support Vector Machines (SVM Radial).</li><li>Random Forest (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.</li><li>These findings highlight the robustness of Random Forest in handling complex datasets for admission prediction and provide actionable insights for improving Emergency Department (ED) management strategies.</li></ul>

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

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