Short and Long term predictions of Hospital emergency department attendances

Jilani, Tahseen Ahmed; Housley, Gemma; Figueredo, Grazziela P.; Tang, Pui-Shan; Hatton, James W.; Shaw, Dominick

doi:10.1016/j.ijmedinf.2019.05.011

Cited by 56 publications

(51 citation statements)

References 32 publications

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“…With an increasing number of CVDs patients putting pressure on the limited medical resources, the prediction of healthcare demands, particularly those associated with peak events, has gained greater attention. Time series forecasting approaches, such as the autoregressive integrated moving average (ARIMA) model and the seasonal ARIMA model, are widely applied in predicting problems regarding emergency department visits [18,19], new admission inpatients [20] and inpatients discharge [21]. However, these models have difficulties solving the complex nonlinear relationship among multi-factors, and their forecasting abilities to extrapolate are limited.…”

Section: Introductionmentioning

confidence: 99%

Machine learning approaches to predict peak demand days of cardiovascular admissions considering environmental exposure

Qiu

Luo

et al. 2020

BMC Med Inform Decis Mak

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Background: Accumulating evidence has linked environmental exposure, such as ambient air pollution and meteorological factors, to the development and severity of cardiovascular diseases (CVDs), resulting in increased healthcare demand. Effective prediction of demand for healthcare services, particularly those associated with peak events of CVDs, can be useful in optimizing the allocation of medical resources. However, few studies have attempted to adopt machine learning approaches with excellent predictive abilities to forecast the healthcare demand for CVDs. This study aims to develop and compare several machine learning models in predicting the peak demand days of CVDs admissions using the hospital admissions data, air quality data and meteorological data in Chengdu, China from 2015 to 2017. Methods: Six machine learning algorithms, including logistic regression (LR), support vector machine (SVM), artificial neural network (ANN), random forest (RF), extreme gradient boosting (XGBoost), and light gradient boosting machine (LightGBM) were applied to build the predictive models with a unique feature set. The area under a receiver operating characteristic curve (AUC), logarithmic loss function, accuracy, sensitivity, specificity, precision, and F1 score were used to evaluate the predictive performances of the six models. Results: The LightGBM model exhibited the highest AUC (0.940, 95% CI: 0.900-0.980), which was significantly higher than that of LR (0.842, 95% CI: 0.783-0.901), SVM (0.834, 95% CI: 0.774-0.894) and ANN (0.890, 95% CI: 0.836-0.944), but did not differ significantly from that of RF (0.926, 95% CI: 0.879-0.974) and XGBoost (0.930, 95% CI: 0.878-0.982). In addition, the LightGBM has the optimal logarithmic loss function (0.218), accuracy (91.3%), specificity (94.1%), precision (0.695), and F1 score (0.725). Feature importance identification indicated that the contribution rate of meteorological conditions and air pollutants for the prediction was 32 and 43%, respectively.

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Section: Introductionmentioning

confidence: 99%

Machine learning approaches to predict peak demand days of cardiovascular admissions considering environmental exposure

Qiu

Luo

et al. 2020

BMC Med Inform Decis Mak

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“…Although the medium and long-term forecasts can achieve a better fitting effect, help managers make sound downstream decisions, and give more buffer time to adapt to the cyclical schedule, they cannot provide good support for the accurate arrangement of medical resources at the operational level [ 12 , 42 ]. Yet, precise short-term forecasts can provide managers with theoretical support for arranging human resources as well as adequate preparation for emergencies, which tends to be more useful [ 43 ]. However, the complexity of daily time series prediction is high because the shorter the prediction period is, the lower the prediction accuracy is [ 38 , 43 ].…”

Section: Introductionmentioning

confidence: 99%

“…Yet, precise short-term forecasts can provide managers with theoretical support for arranging human resources as well as adequate preparation for emergencies, which tends to be more useful [ 43 ]. However, the complexity of daily time series prediction is high because the shorter the prediction period is, the lower the prediction accuracy is [ 38 , 43 ]. Abraham et al [ 44 ] showed that forecasting patient flow in EDs beyond seven days does not yield reliable results.…”

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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“…With the increasing number of CVDs patients, the contradiction between the growing demand of patients for healthcare services and the limited medical resources is becoming prominent, which makes the prediction of future healthcare demand particularly those associated with peak event has gained greater attention. Time series forecasting approaches, such as the autoregressive integrated moving average (ARIMA) model and the seasonal ARIMA (SARIMA) model, are widely applied in predicting problems about emergency department visits [18,19], new admission inpatients [20] and inpatients discharge [21]. However, these models seem to be difficult to solve the complex nonlinear relationship among multi-factors and their forecasting abilities to extrapolate are limited.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Approaches to Predict Peak Demand Days of Cardiovascular Admissions Considering Environmental Exposure

Qiu

Luo

et al. 2019

Preprint

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Background Accumulating evidence has linked environmental exposures, such as ambient air pollution and meteorological factors to the development and severity of cardiovascular diseases (CVDs), resulting in increased healthcare demand. Effective prediction of situations of demand for healthcare services particularly those associated with peak events of CVDs can be useful in optimizing the allocation of medical resources. However, few studies have attempted to adopt machine learning approaches with excellent predictive abilities to forecast the healthcare demand for CVDs. This study aims to develop machine learning models to predict the peak demand days of CVDs admissions using the hospital admissions data, air quality data and meteorological data in Chengdu, China from 2015 to 2017.Methods Six machine learning algorithms, including logistic regression (LR), support vector machine (SVM), random forest (RF), extreme gradient boosting (XGBoost), light gradient boosting machine (LightGBM) and artificial neural network (ANN), were applied to build the predictive models. The area under a receiver operating characteristic curve (AUC), logarithmic loss function, accuracy, sensitivity, specificity and F1 score were used to evaluate the predictive performances among the six models.Results The LightGBM model exhibited the highest AUC (0.940, 95% CI: 0.900-0.980), which was significantly higher than that of LR (0.842, 95% CI: 0.783-0.901), SVM (0.834, 95% CI: 0.774-0.894) and ANN (0.890, 95% CI: 0.836-0.944), but did not differ significantly from that of RF (0.926, 95% CI: 0.879-0.974) and XGBoost (0.930, 95% CI: 0.878-0.982). In addition, the LightGBM has the optimal logarithmic loss function (0.218), accuracy (91.3%), specificity (94.1%) and F1 score (0.725). Feature importance identification based on LightGBM indicated that the contribution rate of meteorological conditions and air pollutants for the prediction was 32% and 43%, respectively.Conclusion This study suggests that ensemble learning models especially the LightGBM model can be used to effectively predict the peak events of CVDs, which provide decision making for medical resource management.

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Short and Long term predictions of Hospital emergency department attendances

Cited by 56 publications

References 32 publications

Machine learning approaches to predict peak demand days of cardiovascular admissions considering environmental exposure

Machine learning approaches to predict peak demand days of cardiovascular admissions considering environmental exposure

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

Machine Learning Approaches to Predict Peak Demand Days of Cardiovascular Admissions Considering Environmental Exposure

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