Forecasting Emergency Department Visits Using Internet Data

Ekström, Andreas; Kurland, Lisa; Farrokhnia, Nasim; Castrèn, Maaret; Nordberg, M. E.

doi:10.1016/j.annemergmed.2014.10.008

Cited by 62 publications

(55 citation statements)

References 25 publications

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“…According to ED data,patients arrive in an unplanned fashion beyond the control of the hospital, and the number of ED visits shows strong periodical, seasonal and stochastic fluctuations driven by factors such as climate, epidemic disease, the type of the day and socio demographic effects [6–8]. Based on these features, researchers used different methods to predict the number of ED visits hourly, daily and monthly [5, 6, 9].…”

Section: Introductionmentioning

confidence: 99%

“…Furthermore, aiming to better capture cyclicity over a period of week in daily time series data, ARIMA models have been extended and modified among which seasonal ARIMA (SARIMA) and multiplicative seasonal ARIMA (MSARIMA) models are the most widely used [3, 7, 21]. In addition to randomness and periodicity, the day of the week effect is also present in daily volume of patients in ED and OD as well as daily number of discharged patients [3, 8], which refers to the situation where the time series at the same time point within 7 days a week during different weeks show non-linear trend variation and some change patterns can be found in the data from Monday to Sunday. Several researchers proposed seasonal regression model to capture the day of the week effect of time series data, but it may be problematic and lead to poor forecasts due to the nonlinear patterns.…”

Section: Introductionmentioning

confidence: 99%

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Hospital daily outpatient visits forecasting using a combinatorial model based on ARIMA and SES models

Luo

Zhang

et al. 2017

BMC Health Serv Res

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BackgroundAccurate forecasting of hospital outpatient visits is beneficial for the reasonable planning and allocation of healthcare resource to meet the medical demands. In terms of the multiple attributes of daily outpatient visits, such as randomness, cyclicity and trend, time series methods, ARIMA, can be a good choice for outpatient visits forecasting. On the other hand, the hospital outpatient visits are also affected by the doctors’ scheduling and the effects are not pure random. Thinking about the impure specialty, this paper presents a new forecasting model that takes cyclicity and the day of the week effect into consideration.MethodsWe formulate a seasonal ARIMA (SARIMA) model on a daily time series and then a single exponential smoothing (SES) model on the day of the week time series, and finally establish a combinatorial model by modifying them. The models are applied to 1 year of daily visits data of urban outpatients in two internal medicine departments of a large hospital in Chengdu, for forecasting the daily outpatient visits about 1 week ahead.ResultsThe proposed model is applied to forecast the cross-sectional data for 7 consecutive days of daily outpatient visits over an 8-weeks period based on 43 weeks of observation data during 1 year. The results show that the two single traditional models and the combinatorial model are simplicity of implementation and low computational intensiveness, whilst being appropriate for short-term forecast horizons. Furthermore, the combinatorial model can capture the comprehensive features of the time series data better.ConclusionsCombinatorial model can achieve better prediction performance than the single model, with lower residuals variance and small mean of residual errors which needs to be optimized deeply on the next research step.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Hospital daily outpatient visits forecasting using a combinatorial model based on ARIMA and SES models

Luo

Zhang

et al. 2017

BMC Health Serv Res

View full text Add to dashboard Cite

show abstract

“…For example, we exclude metrics based on seasonality [11,130], pollen counts [58,165], over-the-counter drug sales volume [82,98], and emergency department visits [52]. …”

Section: Related Workmentioning

confidence: 99%

Measuring Global Disease with Wikipedia

Priedhorsky

Osthus

Daughton

et al. 2017

Proceedings of the 2017 ACM Conference on Computer Supported Cooperative Work and Social Computing

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Effective disease monitoring provides a foundation for effective public health systems. This has historically been accomplished with patient contact and bureaucratic aggregation, which tends to be slow and expensive. Recent internet-based approaches promise to be real-time and cheap, with few parameters. However, the question of when and how these approaches work remains open. We addressed this question using Wikipedia access logs and category links. Our experiments, replicable and extensible using our open source code and data, test the effect of semantic article filtering, amount of training data, forecast horizon, and model staleness by comparing across 6 diseases and 4 countries using thousands of individual models. We found that our minimal-configuration, language-agnostic article selection process based on semantic relatedness is effective for improving predictions, and that our approach is relatively insensitive to the amount and age of training data. We also found, in contrast to prior work, very little forecasting value, and we argue that this is consistent with theoretical considerations about the nature of forecasting. These mixed results lead us to propose that the currently observational field of internet-based disease surveillance must pivot to include theoretical models of information flow as well as controlled experiments based on simulations of disease.

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“…A systematic review by Nuti et al [13] showed that using search engine query data for predicting the incidence of both communicable and noncommunicable diseases is reliable. Search engine data has already been successfully utilized in many ways to study a variety of health phenomena, including depression [14][15][16][17].…”

Section: Introductionmentioning

confidence: 99%

Diurnal Variations of Depression-Related Health Information Seeking: Case Study in Finland Using Google Trends Data (Preprint)

Tana¹,

Kettunen²,

Eirola³

et al. 2017

Preprint

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BACKGROUND Some of the temporal variations and clock-like rhythms that govern several different health-related behaviors can be traced in near real-time with the help of search engine data. This is especially useful when studying phenomena where little or no traditional data exist. One specific area where traditional data are incomplete is the study of diurnal mood variations, or daily changes in individuals’ overall mood state in relation to depression-like symptoms. OBJECTIVE The objective of this exploratory study was to analyze diurnal variations for interest in depression on the Web to discover hourly patterns of depression interest and help seeking. METHODS Hourly query volume data for 6 depression-related queries in Finland were downloaded from Google Trends in March 2017. A continuous wavelet transform (CWT) was applied to the hourly data to focus on the diurnal variation. Longer term trends and noise were also eliminated from the data to extract the diurnal variation for each query term. An analysis of variance was conducted to determine the statistical differences between the distributions of each hour. Data were also trichotomized and analyzed in 3 time blocks to make comparisons between different time periods during the day. RESULTS Search volumes for all depression-related query terms showed a unimodal regular pattern during the 24 hours of the day. All queries feature clear peaks during the nighttime hours around 11 PM to 4 AM and troughs between 5 AM and 10 PM. In the means of the CWT-reconstructed data, the differences in nighttime and daytime interest are evident, with a difference of 37.3 percentage points (pp) for the term “Depression,” 33.5 pp for “Masennustesti,” 30.6 pp for “Masennus,” 12.8 pp for “Depression test,” 12.0 pp for “Masennus testi,” and 11.8 pp for “Masennus oireet.” The trichotomization showed peaks in the first time block (00.00 AM-7.59 AM) for all 6 terms. The search volumes then decreased significantly during the second time block (8.00 AM-3.59 PM) for the terms “Masennus oireet” (P<.001), “Masennus” (P=.001), “Depression” (P=.005), and “Depression test” (P=.004). Higher search volumes for the terms “Masennus” (P=.14), “Masennustesti” (P=.07), and “Depression test” (P=.10) were present between the second and third time blocks. CONCLUSIONS Help seeking for depression has clear diurnal patterns, with significant rise in depression-related query volumes toward the evening and night. Thus, search engine query data support the notion of the evening-worse pattern in diurnal mood variation. Information on the timely nature of depression-related interest on an hourly level could improve the chances for early intervention, which is beneficial for positive health outcomes.

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Forecasting Emergency Department Visits Using Internet Data

Cited by 62 publications

References 25 publications

Hospital daily outpatient visits forecasting using a combinatorial model based on ARIMA and SES models

Hospital daily outpatient visits forecasting using a combinatorial model based on ARIMA and SES models

Measuring Global Disease with Wikipedia

Diurnal Variations of Depression-Related Health Information Seeking: Case Study in Finland Using Google Trends Data (Preprint)

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