Summary of Ef-net: a Deep Learning Approach Combining Word Embeddings and Feature Fusion For Patient Disposition Analysis, by Nafisa Binte Feroz et al.
EF-Net: A Deep Learning Approach Combining Word Embeddings and Feature Fusion for Patient Disposition Analysis
by Nafisa Binte Feroz, Chandrima Sarker, Tanzima Ahsan, K M Arefeen Sultan, Raqeebir Rab
First submitted to arxiv on: 20 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This research aims to develop a prediction model for patient disposition in emergency departments, addressing the issue of overcrowding caused by an aging population and rising healthcare costs. The proposed EF-Net model incorporates categorical features into the neural network layer and adds numerical features with embedded categorical features. Combining EF-Net with XGBoost models achieves higher accuracy. The study utilizes the soft voting technique to generate results. In EF-Net, an accuracy of 95.33% was achieved, while the Ensemble Model reached 96%. The 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. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The researchers aim to solve overcrowding problems in emergency departments by creating a prediction model. This model will help prioritize patients with more serious health issues. The team uses two models: EF-Net and XGBoost. They combine these models to get better results. The study shows that their new model, EF-Net, is very accurate (95.33%) and performs well compared to other models on a specific dataset. |
Keywords
» Artificial intelligence » Ensemble model » F1 score » Neural network » Xgboost
