ArticleChaos, solitons, and fractals2020
Forecasting emergency department overcrowding: A deep learning framework.
Article in Chaos, solitons, and fractals, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed.
- Predicting the Unpredictable: A Data-Driven Machine Learning Model for Emergency Department Waiting Room Surge Status.Mayo Clinic proceedings. Digital health · 2026Article
- Predicting Emergency Department Patient Arrivals at Hospitals Using Machine Learning Techniques.Healthcare (Basel, Switzerland) · 2026Article
- Ascertaining cause-specific emergency department demand using forecast combinations.BMC emergency medicine · 2026Article
- An Artificial Intelligence-Based Framework for Predicting Emergency Department Overcrowding: Development and Evaluation Study.JMIR medical informatics · 2025Article
- Artificial intelligence-driven forecasting and shift optimization for pediatric emergency department crowding.JAMIA open · 2025Article
- Forecasting Mortality Associated Emergency Department Crowding with LightGBM and Time Series Data.Journal of medical systems · 2025Article
- Current Applications, Challenges, and Future Directions of Artificial Intelligence in Emergency Medicine: A Narrative Review.Archives of academic emergency medicine · 2025Article
- Enhanced forecasting of emergency department patient arrivals using feature engineering approach and machine learning.BMC medical informatics and decision making · 2024Article
- The skåne emergency medicine (SEM) cohort.Scandinavian journal of trauma, resuscitation and emergency medicine · 2024Article
- Analyzing and Forecasting Pediatric Fever Clinic Visits in High Frequency Using Ensemble Time-Series Methods After the COVID-19 Pandemic in Hangzhou, China: Retrospective Study.JMIR medical informatics · 2023Article
- Early Warning Software for Emergency Department Crowding.Journal of medical systems · 2023Article
- A Machine Learning Approach for Monitoring and Classifying Healthcare Data-A Case of Emergency Department of KSA Hospitals.International journal of environmental research and public health · 2023Article
- A systematic review of the modelling of patient arrivals in emergency departments.Quantitative imaging in medicine and surgery · 2023Review
- Forecasting Future Monthly Patient Volume using Deep Learning and Statistical Models.Wireless personal communications · 2023Article
- Forecasting daily emergency department arrivals using high-dimensional multivariate data: a feature selection approach.BMC medical informatics and decision making · 2022Article
- A proficient approach to forecast COVID-19 spread via optimized dynamic machine learning models.Scientific reports · 2022Article
- Towards accurate prediction of patient length of stay at emergency department: a GAN-driven deep learning framework.Journal of ambient intelligence and humanized computing · 2022Article
- Deep Generative Learning-Based 1-SVM Detectors for Unsupervised COVID-19 Infection Detection Using Blood Tests.IEEE transactions on instrumentation and measurement · 2022Article
- Deep Learning-Based Intelligent Robot in Sentencing.Frontiers in psychology · 2022Article
- How New Mexico Leveraged a COVID-19 Case Forecasting Model to Preemptively Address the Health Care Needs of the State: Quantitative Analysis.JMIR public health and surveillance · 2021Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
As the demand for medical cares has considerably expanded, the issue of managing patient flow in hospitals and especially in emergency departments (EDs) is certainly a key issue to be carefully mitigated. This can lead to overcrowding and the degradation of the quality of the provided medical services. Thus, the accurate modeling and forecasting of ED visits are critical for efficiently managing the overcrowding problems and enable appropriate optimization of the available resources. This paper proposed an effective method to forecast daily and hourly visits at an ED using Variational AutoEncoder (VAE) algorithm. Indeed, the VAE model as a deep learning-based model has gained special attention in features extraction and modeling due to its distribution-free assumptions and superior nonlinear approximation. Two types of forecasting were conducted: one- and multi-step-ahead forecasting. To the best of our knowledge, this is the first time that the VAE is investigated to improve forecasting of patient arrivals time-series data. Data sets from the pediatric emergency department at Lille regional hospital center, France, are employed to evaluate the forecasting performance of the introduced method. The VAE model was evaluated and compared with seven methods namely Recurrent Neural Network (RNN), Long short-term memory (LSTM), Bidirectional LSTM (BiLSTM), Convolutional LSTM Network (ConvLSTM), restricted Boltzmann machine (RBM), Gated recurrent units (GRUs), and convolutional neural network (CNN). The results clearly show the promising performance of these deep learning models in forecasting ED visits and emphasize the better performance of the VAE in comparison to the other models.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.