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find Keyword "季节" 6 results
  • Comparative Study on Calculation Methods of Seasonal Index for Outpatient Volume in a Hospital

    目的:在移动平均趋势剔除法、最小二乘法两种趋势剔除法中找出一种能较好反映某医院门诊量季节变化规律的方法。方法:根据该医院2005~2008年各月门诊数据,运用移动平均趋势剔除法、最小二乘法分别计算该医院门诊患者的季节指数(seasonal index)和各月预测值,并对预测结果进行平均绝对偏差(MAD)、平均平方误差(MSE)、平均预测误差(AFE)、平均绝对百分误差(MAPE)分析。同时,判断实际值与预测值的容许区间的关系。结果:移动平均趋势剔除法和最小二乘法预测值的MAD、MSE、AFE、MAPE分别为766.94,888236.8542,-0.23,5.478249.8%和739.0196,802281.2,0.125,5.259-453%。移动平均趋势剔除法有4个实际值落在容许区间之外,最小二乘法有2个。结论:最小二乘法能够更好反映出该院门诊量季节变化的规律,是预测的最佳选择方案。

    Release date:2016-09-08 10:01 Export PDF Favorites Scan
  • Study on the Seasonal Distribution of Multidrug-resistant Organism in Neurosurgical Intensive Care Unit

    ObjectiveTo analyze epidemic characteristics of multidrug-resistant organism (MDRO) in Neurosurgical Intensive Care Unit (NSICU), and to analyze the status of infection and colonization, in order to provide reference for constituting intervention measures. MethodsPatients who stayed in NSICU during January 2014 to April 2015 were actively monitored for the MDRO situation. ResultsA total of 218 MDRO pathogens were isolated from 159 patients, and 42 cases were healthcare-associated infections (HAI) among 159 patients. The Acinetobacter baumannii was the most common one in the isolated acinetobacter. Colonization rate was positively correlated with the incidence of HAI. From January to December, there was a significantly increase in the colonization rate, but not in the incidence of HAI. ConclusionThe main MDRO situation is colonization in NSICU. The obvious seasonal variation makes the HAI risk at different levels. So it is necessary that full-time and part-time HAI control staff be on alert, issue timely risk warning, and strengthen risk management. The Acinetobacter baumannii has become the number one target for HAI prevention and control in NSICU, so their apparent seasonal distribution is worthy of more attention, and strict implementation of HAI prevention and control measures should be carried out.

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  • Dynamic Analysis of Outpatient and Emergency Visits in a Large Tertiary Hospital in Guangzhou from 2005 to 2013

    ObjectiveTo acquire the flow law of outpatient and emergency visits in a large general hospital. MethodsBy sampling monthly amount of outpatient and emergency from January 2005 to December 2013 of a large general hospital in Guangzhou, the trend of the time series was analyzed and calculated the seasonal index of the amount of hospital outpatient and emergency visits with the use of long-term trends method. ResultThe flow law of patients in the hospital outpatient and emergency was significantly affected by seasonal factors, and different month had its own variation characters. The seasonal indexes were the highest in March, July, August, November and December (seasonal index >105%), while the lowest in January, February, October (seasonal index <95%). ConclusionBased on analysis of the outpatient and emergency visits and causes with hospitals, decision makers and hospitals should make reasonable allocation of medical resources and provide evidence for the scientific decisions of hospital management. Thus, ensure the safety of patients.

    Release date:2016-10-02 04:54 Export PDF Favorites Scan
  • 缺血性卒中发病的季节性变化及其影响因素

    缺血性卒中是世界范围内致死和致残的主要原因。以往的研究结果显示缺血性卒中的发病具有季节性规律,在不同的地区呈现不同的季节性上升趋势,这可能与当地气温变化及与气温变化有关的血压波动、血液成分变化、感染等有关。为控制缺血性卒中发病的季节性增加,降低缺血性卒中的致残率、致死率,该文对世界各地缺血性卒中发病的季节性变化及其可能的影响因素进行了综述。

    Release date:2017-05-18 01:09 Export PDF Favorites Scan
  • Predictive analysis on discharged patients based on curve estimation and trend-season model

    Objective To explore the predicted precision of discharged patients number using curve estimation combined with trend-season model. Methods Curve estimation and trend-season model were both applied, and the quarterly number of discharged patients of 363 hospital from 2009 to 2015 was collected and analyzed in order to predict discharged patients in 2016. Relative error between predicted value and actual number was also calculated. Results An optimal quadratic regression equation Yt=3 006.050 1+202.350 8×t–3.544 4×t2 was established (Coefficient of determination R2=0.927, P<0.001), and a total of 23 462 discharged patients were predicted based on this equation combined with trend-season model, with a relative error of 1.79% compared to the actual number. Conclusion The curve estimation combined with trend-season model is a convenient and visual tool for predicting analysis. It has a high predicted accuracy in predicting the number of hospital discharged patients or outpatients, which can provide a reference basis for hospital operation and management.

    Release date:2017-10-16 11:25 Export PDF Favorites Scan
  • Application value of SARIMA model in forecasting and analyzing inpatient cases of pediatric limb fractures

    ObjectiveTo establish a forecasting model for inpatient cases of pediatric limb fractures and predict the trend of its variation.MethodsAccording to inpatient cases of pediatric limb fractures from January 2013 to December 2018, this paper analyzed its characteristics and established the seasonal auto-regressive integrated moving average (SARIMA) model to make a short-term quantitative forecast.ResultsA total of 4 451 patients, involving 2 861 males and 1 590 females were included. The ratio of males to females was 1.8 to 1, and the average age was 5.655. There was a significant difference in age distribution between males and females (χ2=44.363, P<0.001). The inpatient cases of pediatric limb fractures were recorded monthly, with predominant peak annually, from April to June and September to October, respectively. Using the data of the training set from January 2013 to May 2018, a SARIMA model of SARIMA (0,1,1)(0,1,1)12 model (white noise test, P>0.05) was identified to make short-term forecast for the prediction set from June 2018 to November 2018, with RMSE=8.110, MAPE=9.386, and the relative error between the predicted value and the actual value ranged from 1.61% to 8.06%.ConclusionsCompared with the actual cases, the SARIMA model fits well with good short-term prediction accuracy, and it can help provide reliable data support for a scientific forecast for the inpatient cases of pediatric limb fractures.

    Release date:2020-07-02 09:18 Export PDF Favorites Scan
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