Summary of Effective Predictive Modeling For Emergency Department Visits and Evaluating Exogenous Variables Impact: Using Explainable Meta-learning Gradient Boosting, by Mehdi Neshat et al.
Effective Predictive Modeling for Emergency Department Visits and Evaluating Exogenous Variables Impact: Using Explainable Meta-learning Gradient Boosting
by Mehdi Neshat, Michael Phipps, Nikhil Jha, Danial Khojasteh, Michael Tong, Amir Gandomi
First submitted to arxiv on: 18 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Neural and Evolutionary Computing (cs.NE)
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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 The proposed Meta-learning Gradient Booster (Meta-ED) approach leverages four foundational learners, including Catboost, Random Forest, Extra Tree, and lightGBoost, alongside a dependable top-level learner, Multi-Layer Perceptron (MLP). This novel method aims to precisely forecast daily Emergency Department (ED) visits by combining the unique capabilities of varied base models. The study evaluates the efficacy of Meta-ED through an extensive comparative analysis involving 23 models, showcasing notable superiority in accuracy at 85.7% and across a range of evaluation metrics. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The researchers developed a new way to predict when people will visit emergency departments using artificial intelligence. They combined different learning methods to create a model that is more accurate than others. This new approach, called Meta-ED, was tested by comparing it with other popular techniques. The results showed that Meta-ED did better than the others in predicting how many people would visit the emergency department. |
Keywords
» Artificial intelligence » Meta learning » Random forest
