Diarrhoea outpatient visits prediction based on time series decomposition and multi-local predictor fusion

Wang, Yongming; Gu, Junzhong; Zhou, Zili; Wang, Zhijin

doi:10.1016/j.knosys.2015.08.013

Cited by 21 publications

(17 citation statements)

References 47 publications

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“…Another disadvantage could be considered the inherent difficulties of representing the human body behavior through the use of phenomenological models. In this context, we found in the literature some interesting works where the predictive models are obtained through time series decomposition, see for example [22] . Hence, in future we plan the use of data from diabetic patients to construct efficient models for predicting the responses when specific meals intake and insulin dosage are given.…”

Section: Discussionmentioning

confidence: 99%

Human endocrine system modeling based on ontologies

Viale

Bora

Benegui

et al. 2016

Knowledge-Based Systems

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This article presents a novel use of an ontological approach of a rigorous generic model of the human endocrine system. It is based on an existing ontology specifically developed for chemical engineering design, named OntoCAPE. It provides most of the necessary concepts for implementing compartmental models of the human endocrine system, such as the UVa/Padova model, 1 accepted by the FDA. 2 We named this extended ontology Bio OntoCape which is connected with MatLab to perform dynamic simulation with the constructed model to predict the impact of the external stimuli such as meals intake and insulin dosage. This mathematical model was chosen because is enough versatile to represent healthy, prediabetic and diabetic persons. The complete system is thought to be helpful for participants from different disciplines, such as, endocrinologists, nutritionists, nurses, engineers and patients among others. In addition, it is envisioned that this development can be extended to configure an e-Health platform for diabetic patients treatment in Argentina. This will help to remotely monitoring patients reducing the personal attendance at hospitals as well as medical budgets.

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Section: Discussionmentioning

confidence: 99%

Human endocrine system modeling based on ontologies

Viale

Bora

Benegui

et al. 2016

Knowledge-Based Systems

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“…Therefore, scholars have paid more attention to the establishment of outpatient demand forecasting models. Existing research has focused on outpatient flow [ 4 , 5 ] and other related departments like internal medicine [ 6 , 7 ], cancer [ 8 ], anxiety disorder [ 3 ], diarrhea [ 9 ], and so on; however, the prediction of outpatient blood sampling room visits was ignored. In addition, the blood sampling room visits have the characteristics of the day-of-the-week effect and seasonality, which are due to the registration system as well as human behavior.…”

Section: Introductionmentioning

confidence: 99%

“…Time series models used for forecasting include autoregressive moving average (ARIMA) models, exponential smoothing models, and structural models. Among them, the ARIMA model, which proved to be relatively fast with a small calculation amount and have high reliability as well as accuracy, has been widely used [ 9 , 12 ]. Ibrahim et al estimated an ARIMA model that can realize the three-month forecasting of incoming patients in an OPD medical laboratory [ 13 ].…”

Section: Introductionmentioning

confidence: 99%

Prediction of Daily Blood Sampling Room Visits Based on ARIMA and SES Model

Zhang

Xiong

et al. 2020

Computational and Mathematical Methods in Medicine

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This paper is aimed at establishing a combined prediction model to predict the demand for medical care in terms of daily visits in an outpatient blood sampling room, which provides a basis for rational arrangement of human resources and planning. On the basis of analyzing the comprehensive characteristics of the randomness, periodicity, trend, and day-of-the-week effects of the daily number of blood collections in the hospital, we firstly established an autoregressive integrated moving average model (ARIMA) model to capture the periodicity, volatility, and trend, and secondly, we constructed a simple exponential smoothing (SES) model considering the day-of-the-week effect. Finally, a combined prediction model of the residual correction is established based on the prediction results of the two models. The models are applied to data from 60 weeks of daily visits in the outpatient blood sampling room of a large hospital in Chengdu, for forecasting the daily number of blood collections about 1 week ahead. The result shows that the MAPE of the combined model is the smallest overall, of which the improvement during the weekend is obvious, indicating that the prediction error of extreme value is significantly reduced. The ARIMA model can extract the seasonal and nonseasonal components of the time series, and the SES model can capture the overall trend and the influence of regular changes in the time series, while the combined prediction model, taking into account the comprehensive characteristics of the time series data, has better fitting prediction accuracy than a single model. The new model can well realize the short-to-medium-term prediction of the daily number of blood collections one week in advance.

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“…Meanwhile, Esmaeil Hadavandia presented a hybrid artificial intelligence model for the development of a Mamdani-type fuzzy-rule-based system to forecast outpatient visits[ 2 ]. Decomposition and multi-local predictor fusion were proposed to predict outpatient consults for diarrhea[ 3 ].…”

Section: Introductionmentioning

confidence: 99%

Forecasting outpatient visits using empirical mode decomposition coupled with back-propagation artificial neural networks optimized by particle swarm optimization

Huang

2017

PLoS ONE

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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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Diarrhoea outpatient visits prediction based on time series decomposition and multi-local predictor fusion

Cited by 21 publications

References 47 publications

Human endocrine system modeling based on ontologies

Human endocrine system modeling based on ontologies

Prediction of Daily Blood Sampling Room Visits Based on ARIMA and SES Model

Forecasting outpatient visits using empirical mode decomposition coupled with back-propagation artificial neural networks optimized by particle swarm optimization

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