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Chinese Journal of Emergency Medicine ; (12): 1427-1431, 2017.
Article in Chinese | WPRIM | ID: wpr-694346


Objective One of the major challenges to emergency department is to provide high quality and time sensitive service under limitation of human/material resources,along with patients population with extremely complex conditions.We presented a study that based on a big data got from real world and used wavelet transform technique to analyze time-dependent diseases spectrum patterns and evolution patterns,which will provide solid methodological support for optimizing resources configuration for acute care surgery service.Methods Record data of patients admitted to acute care surgery from 2007-2014 were collected by using data management tool (Avaintec,Helsinki,Finland).The data were cleansed and were transformed to continuing spectrum according to time series of admission time points (per 9 hours).Matlab was used for wavelet transform,and applied five levels of wavelet decomposition and calculated the best decomposition levels by K-mean algorithm for each level.Then we used aprori algorithm for data mining (frequent patterns mining).Results A total of 23 795 cases were enrolled and acute abdomens were made up biggest proportion of admission.Meanwhile,it is found that the spectrum of acute care surgery admission frequency was a complex rising sequence.After wavelet decomposition,signal wave A reflexed trends evolution in a given time scale,and noise wave D reflexed minutia at relevant time scale.In another words,a principal wave A1 represented fluctuation at a cycle of 16 days.Noise wave D1 reflected intensity level in this 16 days' cycle.For example,the 5 · 12 episodes of massive earthquake in 2008 were included in the study,it is found that a significant noise wave at D3 level that indicated a 4 days' cycle.Clinically,it indicated explosive admissions to acute care surgery in 4 days.Conclusions The admission spectrum to acute care surgery is a phenomenon of multi-scale.Based on wavelet decomposing,we can easily analyze the rule of admission spectrum from electronic records of patients and can be used for optimization the emergency medicine resources.