mlsanityData to 7 Oct 2026

Effective Predictive Modeling for Emergency Department Visits and Evaluating Exogenous Variables Impact: Using Explainable Meta-learning Gradient Boosting

arXiv 2411.11275 ↗First posted 18 Nov 2024cs.LG cs.NEcited by 0

Not on the map. Why?

Abstract (arXiv)

Over an extensive duration, administrators and clinicians have endeavoured to predict Emergency Department (ED) visits with precision, aiming to optimise resource distribution. Despite the proliferation of diverse AI-driven models tailored for precise prognostication, this task persists as a formidable challenge, besieged by constraints such as restrained generalisability, susceptibility to overfitting and underfitting, scalability issues, and complex fine-tuning hyper-parameters. In this study, we introduce a novel Meta-learning Gradient Booster (Meta-ED) approach for precisely forecasting daily ED visits and leveraging a comprehensive dataset of exogenous variables, including socio-demographic characteristics, healthcare service use, chronic diseases, diagnosis, and climate parameters spanning 23 years from Canberra Hospital in ACT, Australia. The proposed Meta-ED consists of four foundational learners-Catboost, Random Forest, Extra Tree, and lightGBoost-alongside a dependable top-level learner, Multi-Layer Perceptron (MLP), by combining the unique capabilities of varied base models (sub-learners). Our study assesses the efficacy of the Meta-ED model through an extensive comparative analysis involving 23 models. The evaluation outcomes reveal a notable superiority of Meta-ED over the other models in accuracy at 85.7% (95% CI ;85.4%, 86.0%) and across a spectrum of 10 evaluation metrics. Notably, when compared with prominent techniques, XGBoost, Random Forest (RF), AdaBoost, LightGBoost, and Extra Tree (ExT), Meta-ED showcases substantial accuracy enhancements of 58.6%, 106.3%, 22.3%, 7.0%, and 15.7%, respectively. Furthermore, incorporating weather-related features demonstrates a 3.25% improvement in the prediction accuracy of visitors' numbers. The encouraging outcomes of our study underscore Meta-ED as a foundation model for the precise prediction of daily ED visitors.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 9 of its 78 references that are in the map. First posted on arXiv 18 Nov 2024.

Builds on

9 of 78 references.

References · 9

  1. 2017Attention Is All You Needcited by 59,932
  2. 2016XGBoost: A Scalable Tree Boosting Systemcited by 5,120
  3. 2021Efficient Deep Learning: A Survey on Making Deep Learning Models Smaller, Faster, and Bettercited by 153
  4. 2021Explainable artificial intelligence (XAI) in deep learning-based medical image analysiscited by 147
  5. 2019GBDT-MO: Gradient Boosted Decision Trees for Multiple Outputscited by 16
  6. 2019Obesity Prediction with EHR Data: A deep learning approach with interpretable elementscited by 15
  7. 2022A Temporal Fusion Transformer for Long-term Explainable Prediction of Emergency Department Overcrowdingcited by 3
  8. 2024Predicting the Stay Length of Patients in Hospitals using Convolutional Gated Recurrent Deep Learning Modelcited by 2
  9. 2022Forecasting Patient Flows with Pandemic Induced Concept Drift using Explainable Machine Learningcited by 1

Built on by

Not cited yet.