Developing a hybrid artificial intelligence model for outpatient visits forecasting in hospitals

Hadavandi, Esmaeil; Shavandi, Hassan; Ghanbari, Arash; Abbasian‐Naghneh, Salman

doi:10.1016/j.asoc.2011.09.018

Cited by 59 publications

(34 citation statements)

References 25 publications

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“…Forecasting the number of patient visits to hospitals can be helpful in allocating limited human and material resources of hospitals (Hadavandi et al, 2012). For instance, forecasting of short-term hospital census may result in improvement of inpatient bed allocation and decrease in the incidence of overstaffing and understaffing (Littig et al, 2007).…”

Section: Introductionmentioning

confidence: 99%

Forecasting Patient Visits to Hospitals using a WD&ANN-based Decomposition and Ensemble Model

Hang

Tang

et al. 2017

EURASIA J MATH SCI T

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Forecasting the number of patient visits to hospitals has aroused an increasingly large interest from both theoretic and application perspectives. To enhance the accuracy of forecasting hospital visits, this paper proposes a hybrid approach by coupling wavelet decomposition (WD) and artificial neural network (ANN) under the framework of "decomposition and ensemble". In this model, the WD is first employed to decompose the original monthly data of the number of patient visits to hospitals into several components and one residual term. Then, the ANN as a powerful prediction tool is implemented to fit each decomposed component and generate individual prediction results. Finally, all individual prediction values are fused into the final prediction output by simple addition method. For illustration and verification, four sets of monthly series data of the number of patient visits to hospitals are used as the sample data, and the results show that the proposed model can obtain significantly more accurate forecasting results than all considered popular forecasting techniques.

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Section: Introductionmentioning

confidence: 99%

Forecasting Patient Visits to Hospitals using a WD&ANN-based Decomposition and Ensemble Model

Hang

Tang

et al. 2017

EURASIA J MATH SCI T

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“…Extensive research has resulted in numerous prediction applications using artificial neural networks (ANN), fuzzy logic and genetic algorithms (GA) and other techniques [12,39,58,44,56,32,33,53,34,25]. Most progress to date in AI has been made in the areas of problem solving; concepts and methods for building programs that reason about problems rather than calculate a solution.…”

Section: Introduction and Literature Reviewmentioning

confidence: 99%

Hybridization of evolutionary Levenberg–Marquardt neural networks and data pre-processing for stock market prediction

Asadi

Hadavandi

Mehmanpazir

et al. 2012

Knowledge-Based Systems

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165

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“…22 Fuzzy prediction methods are suitable under incomplete data conditions and require fewer observations than other prediction models do. Despite the advantages of ANNs, they have weaknesses, one of the most important of which is their requirement for large amount of data to yield accurate results.…”

Section: Introductionmentioning

confidence: 99%

“…In addition, the training procedure of an ANN model is time consuming. 22,26 However, as the system complexity and nonlinearity increase, obtaining a reliable and accurate knowledge base (KB) for fuzzy systems used to describe the system behavior becomes difficult. 23 Considering the uncertainty and complexity in all stock markets, which is related to their short and long-term future states, 24 fuzzy rule based models seem ideal candidates for stock market analysis and prediction.…”

Section: Introductionmentioning

confidence: 99%

A hybrid fuzzy intelligent agent-based system for stock price prediction

Zarandi

Hadavandi

Türkşen

2012

Int. J. Intell. Syst.

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Stock price prediction is an important task for most investors and professional analysts. However, it is a tough problem because of the uncertainties involved in prices. This paper presents a four‐layer fuzzy multiagent system (FMAS) architecture to develop a hybrid artificial intelligence model based on the coordination of intelligent agents performing data preprocessing and function approximation tasks for next‐day stock price prediction. The first layer is dedicated to metadata creation. The second layer is aimed at data preprocessing using stepwise regression analysis and self‐organizing map neural network clustering for modularizing prediction problems. The third layer is aimed at model building for each cluster using genetic fuzzy systems and evaluating built models to choose the best evolved fuzzy system for each cluster. Finally, the fourth layer provides model analysis and knowledge presentation. The capability of FMAS is evaluated by applying it on stock price data gathered from IT and airline sectors and comparing the outcomes with the results of other methods. The results show that FMAS outperforms all previous methods, so it can be considered as a suitable tool for stock price prediction problems. © 2012 Wiley Periodicals, Inc.

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Developing a hybrid artificial intelligence model for outpatient visits forecasting in hospitals

Cited by 59 publications

References 25 publications

Forecasting Patient Visits to Hospitals using a WD&ANN-based Decomposition and Ensemble Model

Forecasting Patient Visits to Hospitals using a WD&ANN-based Decomposition and Ensemble Model

Hybridization of evolutionary Levenberg–Marquardt neural networks and data pre-processing for stock market prediction

A hybrid fuzzy intelligent agent-based system for stock price prediction

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Developing a hybrid artificial intelligence model for outpatient visits forecasting in hospitals

Cited by 59 publications

References 25 publications

Forecasting Patient Visits to Hospitals using a WD&amp;ANN-based Decomposition and Ensemble Model

Forecasting Patient Visits to Hospitals using a WD&amp;ANN-based Decomposition and Ensemble Model

Hybridization of evolutionary Levenberg–Marquardt neural networks and data pre-processing for stock market prediction

A hybrid fuzzy intelligent agent-based system for stock price prediction

Contact Info

Product

Resources

About

Forecasting Patient Visits to Hospitals using a WD&ANN-based Decomposition and Ensemble Model

Forecasting Patient Visits to Hospitals using a WD&ANN-based Decomposition and Ensemble Model