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34 results for “arrival times”
Forecasting hourly emergency department arrival using time series analysis
<p></p> Background/aims <p>The stochastic arrival of patients at hospital emergency departments complicates their management. More than 50% of a hospital's emergency department tends to operate beyond its normal capacity and eventually fails to deliver high-quality care. To address this concern, much research has been carried out using yearly, monthly and weekly time-series forecasting. This article discusses the use of hourly time-series forecasting to help improve emergency department management by predicting the arrival of future patients.</p> Methods <p>Emergency department admission data from January 2014 to August 2017 was retrieved from a hospital in Iowa. The auto-regressive integrated moving average (ARIMA), Holt–Winters, TBATS, and neural network methods were implemented and compared as forecasters of hourly patient arrivals.</p> Results <p>The auto-regressive integrated moving average (3,0,0) (2,1,0) was selected as the best fit model, with minimum Akaike information criterion and Schwartz Bayesian criterion. The model was stationary and qualified under the Box–Ljung correlation test and the Jarque–Bera test for normality. The mean error and root mean square error were selected as performance measures. A mean error of 1.001 and a root mean square error of 1.55 were obtained.</p> Conclusions <p>The auto-regressive integrated moving average can be used to provide hourly forecasts for emergency department arrivals and can be implemented as a decision support system to aid staff when scheduling and adjusting emergency department arrivals.</p> <p></p><p></p><p></p>
Cumulative arrival time distribution data for "Upscaling transport in heterogeneous media featuring local-scale dispersion: flow channeling, macro-retardation and parameter prediction"
<div> <div>This archive contains arrival time CDF data for a variety of transport simulations in heterogeneous Darcy flow fields, alongside metadata describing the flow fields. The flow fields were spatially periodic, intersected by uniformly-spaced imaginary planes. Arrival times represent length of time from particle departure from one plane until arrival at the next.</div> <div> </div> <div>Consult the README.md file at the top level of the archive for more information. The file format used to store the CDF data is documented in the Python script at the top level of the archive.</div> </div>
Data for "A Method of Three-Dimensional Location for LFEDA Combining the Time of Arrival Method and the Time Reversal Technique"
<p>The data used to generate and be displayed in figures and tables in this manuscript can be downloaded here. Please use Matlab to open files in mat format and use Microsoft Excel to open files in xlsx format. The data can be used freely for scientific purposes with appropriate citation.</p>
Data from: Timing of arrival in the breeding area is repeatable and affects reproductive success in a non-migratory population of blue tits
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Data from: Sex-specific arrival times on the breeding grounds: hybridizing migratory skuas provide empirical support for the role of sex ratios
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Data from: Timing of mutualist arrival has a greater effect on Pinus muricata seedling growth than interspecific competition
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Forecasting hourly emergency department arrival using time series analysis
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Data from: Factors related to time of stroke onset versus time of hospital arrival: A SITS registry-based study in an Egyptian Stroke Center
<p><b>Background: </b>high-quality data on time of stroke onset and time of hospital arrival is required for proper evaluation of points of delay that might hinder access to medical care after the onset of stroke symptoms.</p> <p><b>Purpose: </b>Based on (SITS Dataset) in Egyptian stroke patients, we aimed to explore factors related to time of onset versus time of hospital arrival for acute ischemic stroke (AIS).</p> <p><span><b>Material and Methods:</b> We included 1,450 AIS patients from two stroke centers of Ain Shams University, Cairo, Egypt. We divided the day to four quarters and evaluated relationship between different factors and time of stroke onset and time of hospital arrival. The factors included: age, sex, duration from stroke onset to hospital arrival, type of management, type of stroke (TOAST classification), National Institute of Health Stroke Scale (NIHSS) on admission and favorable outcome modified Rankin Scale (mRS ≤2). </span></p> <p><span><b>Results: </b>Pre-hospital: highest stroke incidence was in the first and fourth quarters. There was no significant difference in the mean age, sex, type of stroke in relation to time of onset. NIHSS was significantly less in onset in third quarter of the day. Percentage of patients who received thrombolytic therapy was higher with onset in the first 2 quarters of the day (p=<0.001). In-hospital: there was no difference in percentage of patients who received thrombolytic therapy nor in outcome across 4 quarters of arrival to hospital.</span></p> <p><span><b>Conclusion:</b> pre-hospital factors still need adjustment to improve percentage of thrombolysis, while in-hospital factors showed consistent performance.</span></p>
Data from: Predation can select for later and more synchronous arrival times in migrating species
For migratory species, the timing of arrival at breeding grounds is an important determinant of fitness. Too early arrival at the breeding ground is associated with various costs, and we focus on one understudied cost: that migrants can experience a higher risk of predation if arriving earlier than the bulk of the breeding population. We show, using both a semi-analytic and simulation model, that predation can select for later arrival. This is because of safety in numbers: predation risk becomes diluted if many other individuals, either con- or heterospecific, are already residing in the area. Predation risk dilution can also select for more synchronous arrival because deviating from the current population-wide norm to earlier or later dates leads to higher predation risk or to failures in territory acquisition, respectively. The fact that selection for high arrival synchrony can in some cases be more important than selection for a specific date (early or late) within the season is an example of an 'evolutionary priority effect': whichever strategy – in this case a particular arrival time – becomes established in a population can remain stable over long periods of time; there are many possible equilibria (multiple stable states) which the population can remain at. Mixed arrival strategies are also possible under some circumstances.
Data from: Predation can select for later and more synchronous arrival times in migrating species
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Data from: Factors related to time of stroke onset versus time of hospital arrival: A SITS registry-based study in an Egyptian Stroke Center
Open the record for dataset details and reuse information.
Neural network predicted phase arrival times for stations within and around San Juan Basin
<p>Neural network predicted phase arrival times for stations within and around San Juan Basin. This dataset is not manually reviewed and may contain a large number of false positives.</p>
Shorten the Time Required to Correct the Arrival of Complete Oral Feeding in Premature Infants
ClinicalTrials.gov study NCT05208437. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Arrival Time Implementation
ClinicalTrials.gov study NCT07314697. IPD Sharing: NO. Countries: 1. Publications: 0.
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