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

arXiv: https://arxiv.org/abs/2503.22706 · HTML: https://mlsanity.com/paper/2503.22706/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 87 references that are in the map.
- First posted on arXiv 18 Mar 2025.

## Builds on (most cited first, 5 of 5)

- [An Extensive Data Processing Pipeline for MIMIC-IV](https://mlsanity.com/paper/2204.13841/) (2022)
- [Benchmarking emergency department triage prediction models with machine learning and large public electronic health records](https://mlsanity.com/paper/2111.11017/) (2021)
- [Three-stage intelligent support of clinical decision making for higher trust, validity, and explainability](https://mlsanity.com/paper/2007.12870/) (2020)
- [Benchmarking with MIMIC-IV, an irregular, spare clinical time series dataset](https://mlsanity.com/paper/2401.15290/) (2024)
- [Using machine learning techniques to predict hospital admission at the emergency department](https://mlsanity.com/paper/2106.12921/) (2021)

## Abstract (arXiv)

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.