EF-Net: A Deep Learning Approach Combining Word Embeddings and Feature Fusion for Patient Disposition Analysis

TL;DR

This paper introduces EF-Net, a deep learning model that combines word embeddings and feature fusion to accurately predict patient disposition in emergency departments, outperforming existing methods on the MIMIC-IV-ED dataset.

Contribution

The paper presents a novel neural network model that integrates categorical and numerical features for improved patient disposition prediction.

Findings

EF-Net achieved 95.33% accuracy.

Ensemble model reached 96% accuracy.

Outperformed existing methods on multiple metrics.

Abstract

One of the most urgent problems is the overcrowding in emergency departments (EDs), caused by an aging population and rising healthcare costs. Patient dispositions have become more complex as a result of the strain on hospital infrastructure and the scarcity of medical resources. Individuals with more dangerous health issues should be prioritized in the emergency room. Thus, our research aims to develop a prediction model for patient disposition using EF-Net. This model will incorporate categorical features into the neural network layer and add numerical features with the embedded categorical features. We combine the EF-Net and XGBoost models to attain higher accuracy in our results. The result is generated using the soft voting technique. In EF-Net, we attained an accuracy of 95.33%, whereas in the Ensemble Model, we achieved an accuracy of 96%. The experiment's analysis shows that EF-Net surpasses existing works in accuracy, AUROC, and F1-Score on the MIMIC-IV-ED dataset, demonstrating its potential as a scalable solution for patient disposition assessment. Our code is available at https://github.com/nafisa67/thesis