摘要
目的探讨长短期记忆神经网络模型(Long Short Term Memory Neural Networks,LSTM)在医用耗材需求量管理中的应用,分析和预测未来一段时间内医用常规耗材的使用需求,实现医用耗材需求量的精细化管理。方法采用LSTM模型对无锡市某三甲医院静脉留置针2015—2021年的库房领用量进行分析,预测未来一季度及半年的领用情况。结果根据2019年与2021年预测结果对比及模型各评价指标的分析,发现平均绝对百分比误差最小值是2.27%,最大值为4.54%,出现在2021年预测半年,所有预测平均绝对误差均不超过5%,受到新冠疫情影响时预测精度下降有限。结论LSTM神经网络模型能够较为准确地进行医院医用耗材的需求量预测,可作为医用耗材的库存基数与采购策略制定的参考数据。
Objective To explore the application of long short term memory neural networks(LSTM)in the demand management of medical consumables,to analyze and predict the usage demand of medical routine consumables in the future,and to realize the fine management of the demand of medical consumables.Methods The LSTM model was used to analyze the amount of intravenous indwelling needles received by the depot of a tertiary hospital in Wuxi from 2015 to 2021,and to predict the future quarterly or even half-yearly receipt.Results Based on the comparison of the prediction results between 2019 and 2021 and the analysis of each evaluation index of the model,it was found that the minimum average absolute percentage error was 2.27%,and the maximum value was 4.54%in the six months of 2021.The average absolute percentage error of all the predictions was less than 5%,and the prediction accuracy was limited when affected by COVID-19.Conclusion The LSTM neural network model can predict the demand of medical consumables in hospitals more accurately,and can be used as reference data for the inventory base and procurement strategy development of medical consumables.
作者
杨燕
钱正瑛
庄希
金伟
YANG Yan;QIAN Zhengying;ZHUANG Xi;JIN Wei(Department of Medical Engineering,Wuxi People’s Hospital Affiliated to Nanjing Medical University,Nanjing Jiangsu 214023,China)
出处
《中国医疗设备》
2022年第6期123-126,共4页
China Medical Devices
基金
南京医科大学科技发展基金项目(NMUB2019279)。
关键词
长短期记忆神经网络模型
医用耗材需求管理
新冠疫情
long short term memory neural networks
medical consumables demand management
COVID-19 pandemic
