ArticlePloS one2017
Forecasting outpatient visits using empirical mode decomposition coupled with back-propagation artificial neural networks optimized by particle swarm optimization.
Article in PloS one, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Predicting hospital outpatient volume using XGBoost: a machine learning approach.Scientific reports · 2025Article
- An optimization protocol for MRI examination resource allocation based on demand forecasting and linear programming.Scientific reports · 2025Article
- Prediction of Patient Visits for Skin Diseases through Enhanced Evolutionary Computation and Ensemble Learning.Journal of medical systems · 2025Article
- Daily surgery caseload prediction: towards improving operating theatre efficiency.BMC medical informatics and decision making · 2022Article
- Effectiveness of cascading time series models based on meteorological factors in improving health risk prediction.Environmental science and pollution research international · 2022Article
- Forecast of Outpatient Visits to a Tertiary Eyecare Network in India Using the EyeSmart Electronic Medical Record System.Healthcare (Basel, Switzerland) · 2021Article
- Medical service demand forecasting using a hybrid model based on ARIMA and self-adaptive filtering method.BMC medical informatics and decision making · 2020Article
- Artificial intelligence and the future of global health.Lancet (London, England) · 2020Review
- Prediction of Daily Blood Sampling Room Visits Based on ARIMA and SES Model.Computational and mathematical methods in medicine · 2020Article
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2 authors.
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Abstract
Accurately predicting the trend of outpatient visits by mathematical modeling can help policy makers manage hospitals effectively, reasonably organize schedules for human resources and finances, and appropriately distribute hospital material resources. In this study, a hybrid method based on empirical mode decomposition and back-propagation artificial neural networks optimized by particle swarm optimization is developed to forecast outpatient visits on the basis of monthly numbers. The data outpatient visits are retrieved from January 2005 to December 2013 and first obtained as the original time series. Second, the original time series is decomposed into a finite and often small number of intrinsic mode functions by the empirical mode decomposition technique. Third, a three-layer back-propagation artificial neural network is constructed to forecast each intrinsic mode functions. To improve network performance and avoid falling into a local minimum, particle swarm optimization is employed to optimize the weights and thresholds of back-propagation artificial neural networks. Finally, the superposition of forecasting results of the intrinsic mode functions is regarded as the ultimate forecasting value. Simulation indicates that the proposed method attains a better performance index than the other four methods.
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