A Hybrid Approach for Forecasting Patient Visits in Emergency Department

Xu, Qinneng; Tsui, Kwok‐Leung; Jiang, Wei; Guo, Hainan

doi:10.1002/qre.2095

Cited by 46 publications

(53 citation statements)

References 19 publications

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“…Thus, some studies combine two or more forecasting approaches to benefit from their individual advantages and improve results. Xu et al (2016) combined ARIMA and linear regression (LR) to forecast patients' visits in two EDs in China. Their results show that ARIMA-LR outperforms GLM, ARIMA and ARIMA with explanatory variable (ARIMAX) and hybrid of ARIMA-ANN in one-day ahead forecasting.…”

Section: K 499mentioning

confidence: 99%

Patient visit forecasting in an emergency department using a deep neural network approach

Yousefi

Fathi

et al. 2019

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Purpose This study aims to investigate the factors affecting daily demand in an emergency department (ED) and to provide a forecasting tool in a public hospital for horizons of up to seven days. Design/methodology/approach In this study, first, the important factors to influence the demand in EDs were extracted from literature then the relevant factors to the study are selected. Then, a deep neural network is applied to constructing a reliable predictor. Findings Although many statistical approaches have been proposed for tackling this issue, better forecasts are viable by using the abilities of machine learning algorithms. Results indicate that the proposed approach outperforms statistical alternatives available in the literature such as multiple linear regression, autoregressive integrated moving average, support vector regression, generalized linear models, generalized estimating equations, seasonal ARIMA and combined ARIMA and linear regression. Research limitations/implications The authors applied this study in a single ED to forecast patient visits. Applying the same method in different EDs may give a better understanding of the performance of the model to the authors. The same approach can be applied in any other demand forecasting after some minor modifications. Originality/value To the best of the knowledge, this is the first study to propose the use of long short-term memory for constructing a predictor of the number of patient visits in EDs.

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Section: K 499mentioning

confidence: 99%

Patient visit forecasting in an emergency department using a deep neural network approach

Yousefi

Fathi

et al. 2019

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“…In [18], flow data from one ED in Brazil was used to show that simple seasonal exponential smoothing was the most accurate at jointly predicting arrival rates for all triage levels, while seasonal ARIMA was more accurate at predicting arrival rates for a specific triage level. In [34], a hybrid model of ARIMA and linear regression was developed that outperforms variations of either model in predicting arrival rates at two EDs in China. In [19], flow data from one ED in China was used to show that a combination of single exponential smoothing and seasonal ARIMA is more accurate than either model alone.…”

Section: Other Related Workmentioning

confidence: 99%

PatientFlowNet: A Deep Learning Approach to Patient Flow Prediction in Emergency Departments

Sharafat

Bayati

2021

IEEE Access

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Emergency Department (ED) crowding is a major public health challenge since it can seriously impact patient outcomes; and accurate prediction of patient flow in EDs is essential for improving operational efficiency and quality of care. We present a deep learning framework to predict patient flow rates in EDs, namely the rates of arrival, treatment, and discharge for patients across all triage levels. Our model detects short-term and long-term temporal dependencies within the time-series data of a given patientflow variable, as well as dependencies between time-series data of different patient-flow variables. We implement this framework as a convolutional neural network, which we call PatientFlowNet. Our proposed model learns simultaneously from multiple flow variables over a long temporal window and predicts future values of arrival, treatment, and discharge rates in the ED. We benchmark our model against state-of-the-art methods on data from EDs in three different hospitals. Results show that PatientFlowNet achieves superior prediction accuracy, compared to the baseline methods, and yields a mean absolute error that is 4.8% lower than the leading baseline. Furthermore, we provide a visual and interpretable representation of the learned dependencies by our model, between patient-flow variables in EDs. INDEX TERMS Health information management, machine learning, neural networks, public healthcare, supervised learning.

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“…A basic requirement for predicting timeseries with ARIMA is that the time-series should be stationary or, at the very least, trendstationary [3,7]. A stationary series is one that has no trend, and where variations around the mean have a constant amplitude, e.g., [1,13,14]. Although ARIMA expects a stationary stochastic process as input, very few datasets are natively in such format, thus the use of differencing to "stationarise" is in the model identification stage [1,7] (Figure 1).…”

Section: Autoregressive Integrated Moving Average (Arima)mentioning

confidence: 99%

A Comparison of Time-Series Predictions for Healthcare Emergency Department Indicators and the Impact of COVID-19

2021

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Across the world, healthcare systems are under stress and this has been hugely exacerbated by the COVID pandemic. Key Performance Indicators (KPIs), usually in the form of time-series data, are used to help manage that stress. Making reliable predictions of these indicators, particularly for emergency departments (ED), can facilitate acute unit planning, enhance quality of care and optimise resources. This motivates models that can forecast relevant KPIs and this paper addresses that need by comparing the Autoregressive Integrated Moving Average (ARIMA) method, a purely statistical model, to Prophet, a decomposable forecasting model based on trend, seasonality and holidays variables, and to the General Regression Neural Network (GRNN), a machine learning model. The dataset analysed is formed of four hourly valued indicators from a UK hospital: Patients in Department; Number of Attendances; Unallocated Patients with a DTA (Decision to Admit); Medically Fit for Discharge. Typically, the data exhibit regular patterns and seasonal trends and can be impacted by external factors such as the weather or major incidents. The COVID pandemic is an extreme instance of the latter and the behaviour of sample data changed dramatically. The capacity to quickly adapt to these changes is crucial and is a factor that shows better results for GRNN in both accuracy and reliability.

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A Hybrid Approach for Forecasting Patient Visits in Emergency Department

Cited by 46 publications

References 19 publications

Patient visit forecasting in an emergency department using a deep neural network approach

Patient visit forecasting in an emergency department using a deep neural network approach

PatientFlowNet: A Deep Learning Approach to Patient Flow Prediction in Emergency Departments

A Comparison of Time-Series Predictions for Healthcare Emergency Department Indicators and the Impact of COVID-19

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