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• W2903763522 abstract "The observation of data which counts the arrivals at the ED in scales of hours or days reveals that these arrivals are characterized by the phenomenon of burstiness. The burstiness is a phenomenon that appears in the majority of Emergencies due to a batches of arrivals coming to the ED in a small time interval over a wide scales. The prediction of congestion in ED caused by the batched arrivals seems important to the medical staff. The proper modeling data like arrivals process that has this scaling property is relevant with the self similar time series. The goal of our study is to establish that the time series of patient flow displays fractal behavior, whose quantitative characteristics vary with time. To discover whether a Patients flow has begun to congest, we analyze the time series of patients arrivals data with a Multi- Fractal Detrended Fluctuation (MF-DFA) algorithm." @default.
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• W2903763522 date "2018-01-01" @default.
• W2903763522 modified "2023-09-27" @default.
• W2903763522 title "Emergency Department Overcrowding Detection by a Multifractal Analysis" @default.
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• W2903763522 doi "https://doi.org/10.1016/j.ifacol.2018.11.659" @default.
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