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徐雯, 方铮, 杨旅军. R语言时间序列和自回归积分滑动平均模型预测麻醉耗材领用的研究[J]. 实用临床医药杂志, 2021, 25(15): 18-21, 26. DOI: 10.7619/jcmp.20212104
引用本文: 徐雯, 方铮, 杨旅军. R语言时间序列和自回归积分滑动平均模型预测麻醉耗材领用的研究[J]. 实用临床医药杂志, 2021, 25(15): 18-21, 26. DOI: 10.7619/jcmp.20212104
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XU Wen, FANG Zheng, YANG Lyujun. Research on R language time series and autoregressive integrated moving average model for predication of receiving and use of anesthetic consumables[J]. Journal of Clinical Medicine in Practice, 2021, 25(15): 18-21, 26. DOI: 10.7619/jcmp.20212104
Citation: XU Wen, FANG Zheng, YANG Lyujun. Research on R language time series and autoregressive integrated moving average model for predication of receiving and use of anesthetic consumables[J]. Journal of Clinical Medicine in Practice, 2021, 25(15): 18-21, 26. DOI: 10.7619/jcmp.20212104
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R语言时间序列和自回归积分滑动平均模型预测麻醉耗材领用的研究

Research on R language time series and autoregressive integrated moving average model for predication of receiving and use of anesthetic consumables

    摘要
  • 摘要:
      目的  运用自回归积分滑动平均模型(ARIMA)建立适合的老年患者特色手术科室耗材领用支出的医学经济学模型，预测麻醉科耗材需求的变化趋势。
      方法  采用R软件对本院麻醉科2013年1月—2019年12月耗材领用支出数据建立ARIMA模型，将2020年1—12月耗材领用支出的实际值与预测值分别进行比较，评价模型的预测性能。
      结果  本院麻醉科耗材领用支出在每年2月出现最低值，5月呈现最高峰。建立ARIMA (0，1，1)(0，0，1)12模型对麻醉科耗材需求进行预测，ARIMA模型较好地拟合和预测了周期性波动。ARIMA (0，1，1)(0，0，1)12模型预测的耗材支出在2020年1—12月会有小幅波动。
      结论  ARIMA (0，1，1)(0，0，1)12模型较好地拟合了麻醉科的耗材需求，有助于优化科室决策支持系统及老年择期手术患者围术期护理管理。

     

    Abstract:
      Objective  To establish a suitable medical economics model of receiving and use of specialized surgery consumables in the elderly patient in Department of Anesthesiology by using the autoregressive integrated moving average (ARIMA) model, and to predict the changing trend of the consumables demand in Department of Anesthesiology.
      Methods  R software was used to establish the ARIMA model based on the data of consumables acquisition and expenditure of Anesthesiology Department in authors' hospital from January 2013 to December 2019. The actual value and the predicted value of consumables acquisition and expenditure were compared from January to December 2020, and prediction performance of the model was evaluated.
      Results  The consumption expenditure of Anesthesiology Department in authors' hospital was the lowest in February and the highest in May every year. ARIMA(0, 1, 1)(0, 0, 1)12 model was established to predict the consumable demand of Anesthesiology Department, and ARIMA model met and predicted the periodic fluctuation well. ARIMA(0, 1, 1)(0, 0, 1)12 model predicted that the consumption of consumables will fluctuate slightly from January to December in 2020.
      Conclusion  ARIMA(0, 1, 1)(0, 0, 1)12 model can better fit the needs of consumables in Anesthesiology Department, which is helpful to optimize the department decision support system and perioperative nursing management of elderly patients undergoing elective surgery.

     

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