Forecasting emergency department overcrowding: A deep learning framework

Harrou, Fouzi; Dairi, Abdelkader; Kadri, Farid; Sun, Ying

doi:10.1016/j.chaos.2020.110247

Cited by 46 publications

(29 citation statements)

References 46 publications

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“…Artificial intelligence (AI) can be defined as the simulation of human intelligence in machines programmed to react and work by mimicking the human brain. AI has confirmed its effectiveness in several areas, including healthcare, where it has generally been referred to as a powerful tool to help recognize diseases and make medical diagnoses [4]- [6]. In this pandemic, AI could help predict outbreaks and help assemble quickly evolving data to support general health specialists in complex decision-making [7].…”

Section: Introductionmentioning

confidence: 99%

Predicting COVID-19 Spread using Simple Time-Series Statistical Models

Khayyat

Harrou

Sun

2021

2021 International Conference on ICT for Smart Society (ICISS)

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Accurate and timely forecasts of new COVID-19 cases and recoveries would assist in the management of medical resources and bolster public policy formulation during the current pandemic. This study aims to forecast records of confirmed time-series data using simple time series models. Importantly, to predict COVID-19 data of limited size, the performance of statistical time series models, including Linear Regression (LR) and Exponential Smoothing (ES), was investigated. The daily records of confirmed and recovered cases from Saudi Arabia, India, and France were adopted to train and test the investigated models. The forecasting accuracy has been assessed based on three commonly used statistical indicators. Results reveal that the LR model did not forecast COVID-19 time-series data successfully. On the other hand, the ES model showed a promising forecasting performance for both recovered and confirmed timesseries data. Furthermore, results showed that ES outperformed the Decision Tree regression and support vector regression with linear kernel.

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Section: Introductionmentioning

confidence: 99%

Predicting COVID-19 Spread using Simple Time-Series Statistical Models

Khayyat

Harrou

Sun

2021

2021 International Conference on ICT for Smart Society (ICISS)

Self Cite

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“…In Harrou et al. ( 2020a ), authors applied a particularly promising deep learning-based model called a variational autoencoder (VAE) to the problem of predicting patient admissions and flow through an emergency department in a pediatric hospital. Results show that the VAE model has a much better performance than the other models.…”

Section: Introductionmentioning

confidence: 99%

Towards accurate prediction of patient length of stay at emergency department: a GAN-driven deep learning framework

Kadri

Dairi

Harrou

et al. 2022

J Ambient Intell Human Comput

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Recently, the hospital systems face a high influx of patients generated by several events, such as seasonal flows or health crises related to epidemics (e.g., COVID’19). Despite the extent of the care demands, hospital establishments, particularly emergency departments (EDs), must admit patients for medical treatments. However, the high patient influx often increases patients’ length of stay (LOS) and leads to overcrowding problems within the EDs. To mitigate this issue, hospital managers need to predict the patient’s LOS, which is an essential indicator for assessing ED overcrowding and the use of the medical resources (allocation, planning, utilization rates). Thus, accurately predicting LOS is necessary to improve ED management. This paper proposes a deep learning-driven approach for predicting the patient LOS in ED using a generative adversarial network (GAN) model. The GAN-driven approach flexibly learns relevant information from linear and nonlinear processes without prior assumptions on data distribution and significantly enhances the prediction accuracy. Furthermore, we classified the predicted patients’ LOS according to time spent at the pediatric emergency department (PED) to further help decision-making and prevent overcrowding. The experiments were conducted on actual data obtained from the PED in Lille regional hospital center, France. The GAN model results were compared with other deep learning models, including deep belief networks, convolutional neural network, stacked auto-encoder, and four machine learning models, namely support vector regression, random forests, adaboost, and decision tree. Results testify that deep learning models are suitable for predicting patient LOS and highlight GAN’s superior performance than the other models.

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“…Hospitals were also developing short-term forecasting tools of total occupancy or bed use [19][20][21], total occupancy as predicted by emergency department visits [22], and various emergency department metrics [23][24][25][26]. These short-term prediction efforts tended to approach the problem either from a hospital administration perspective, by focusing on operational measures informed by a single hospital's [19][20][21][22] or department's [23][24][25][26] historic data or surgery schedule [27], or from a research perspective by using hospital time series data as a use case for the development and assessment of novel statistical models [28][29][30][31] without regard to hospitals as complex systems in response to a burgeoning pandemic.…”

Section: Introductionmentioning

confidence: 99%

How New Mexico Leveraged a COVID-19 Case Forecasting Model to Preemptively Address the Health Care Needs of the State: Quantitative Analysis

Castro¹,

Shelley²,

Osthus³

et al. 2021

JMIR Public Health Surveill

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Background Prior to the COVID-19 pandemic, US hospitals relied on static projections of future trends for long-term planning and were only beginning to consider forecasting methods for short-term planning of staffing and other resources. With the overwhelming burden imposed by COVID-19 on the health care system, an emergent need exists to accurately forecast hospitalization needs within an actionable timeframe. Objective Our goal was to leverage an existing COVID-19 case and death forecasting tool to generate the expected number of concurrent hospitalizations, occupied intensive care unit (ICU) beds, and in-use ventilators 1 day to 4 weeks in the future for New Mexico and each of its five health regions. Methods We developed a probabilistic model that took as input the number of new COVID-19 cases for New Mexico from Los Alamos National Laboratory’s COVID-19 Forecasts Using Fast Evaluations and Estimation tool, and we used the model to estimate the number of new daily hospital admissions 4 weeks into the future based on current statewide hospitalization rates. The model estimated the number of new admissions that would require an ICU bed or use of a ventilator and then projected the individual lengths of hospital stays based on the resource need. By tracking the lengths of stay through time, we captured the projected simultaneous need for inpatient beds, ICU beds, and ventilators. We used a postprocessing method to adjust the forecasts based on the differences between prior forecasts and the subsequent observed data. Thus, we ensured that our forecasts could reflect a dynamically changing situation on the ground. Results Forecasts made between September 1 and December 9, 2020, showed variable accuracy across time, health care resource needs, and forecast horizon. Forecasts made in October, when new COVID-19 cases were steadily increasing, had an average accuracy error of 20.0%, while the error in forecasts made in September, a month with low COVID-19 activity, was 39.7%. Across health care use categories, state-level forecasts were more accurate than those at the regional level. Although the accuracy declined as the forecast was projected further into the future, the stated uncertainty of the prediction improved. Forecasts were within 5% of their stated uncertainty at the 50% and 90% prediction intervals at the 3- to 4-week forecast horizon for state-level inpatient and ICU needs. However, uncertainty intervals were too narrow for forecasts of state-level ventilator need and all regional health care resource needs. Conclusions Real-time forecasting of the burden imposed by a spreading infectious disease is a crucial component of decision support during a public health emergency. Our proposed methodology demonstrated utility in providing near-term forecasts, particularly at the state level. This tool can aid other stakeholders as they face COVID-19 population impacts now and in the future.

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Forecasting emergency department overcrowding: A deep learning framework

Cited by 46 publications

References 46 publications

Predicting COVID-19 Spread using Simple Time-Series Statistical Models

Predicting COVID-19 Spread using Simple Time-Series Statistical Models

Towards accurate prediction of patient length of stay at emergency department: a GAN-driven deep learning framework

How New Mexico Leveraged a COVID-19 Case Forecasting Model to Preemptively Address the Health Care Needs of the State: Quantitative Analysis

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