| 基于多因素灰色模型的上海市基层医疗机构诊疗量预测 |
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| 引用本文： | 裴佳佳,刘媛华.基于多因素灰色模型的上海市基层医疗机构诊疗量预测[J].教育技术导刊,2019,18(6):130-134. |
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| 作者姓名： | 裴佳佳 刘媛华 |
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| 作者单位： | 上海理工大学 管理学院，上海 200093 |
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| 基金项目： | 国家自然科学基金项目（11505114） |
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| 摘 要： | 建立分级诊疗制度是我国医改“十三五”规划五大任务之首，而基层医疗卫生机构承担着分级诊疗的基础任务，是基本医疗卫生服务和公共卫生服务的双重承载，如何提高基层医疗卫生机构服务水平具有重大研究意义。选取上海市2010 -2016年基层医疗机构诊疗数据，建立基于多因素影响的上海市基层医疗机构诊疗量预测组合模型。首先运用灰色关联分析对各影响因素与诊疗量的相关性进行排序，筛选出主要影响因素变量；然后应用GM（1，N）模型对各年度诊疗量进行预测，并利用改进粒子群算法进行背景值优化，以提高预测准确性；最后运用该模型预测2017 -2020年诊疗量。仿真实验结果表明，该模型较单一的GM（1，N）模型准确性更高，预测有效可行。
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| 关 键 词： | 医疗机构诊疗量预测 灰色关联分析 GM（1 N）模型 PSO 背景值优化 |
| 收稿时间： | 2018-10-07 |
Forecast of Diagnosis and Treatment Volume of Shanghai Primary Medical Institutions Based on Multi-factor Grey Model |
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| PEI Jia-jia,LIU Yuan-hua.Forecast of Diagnosis and Treatment Volume of Shanghai Primary Medical Institutions Based on Multi-factor Grey Model[J].Introduction of Educational Technology,2019,18(6):130-134. |
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| Authors: | PEI Jia-jia LIU Yuan-hua |
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| Institution: | Business School，University of Shanghai for Science & Technology，Shanghai 200093，China |
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| Abstract: | The establishment of a grading diagnosis and treatment system is the first of the five major tasks of the 13th Five-Year Plan for medical reform in China. The primary health care institutions are responsible for the basic tasks of grading diagnosis and treatment. They are the dual burdens of basic medical and public health services and how to improve the primary health care institutions. Service levels have significant research implications. This paper selects the data of primary and secondary medical institutions in Shanghai from 2010 to 2016， and establishes a combined model for the diagnosis and treatment of primary care institutions in Shanghai based on multi-factor effects. Firstly， the gray correlation analysis is used to sort the correlation between each influencing factor and the amount of diagnosis and treatment， and the main influencing factors are selected. Then the GM（1，N） model is used to predict the annual diagnosis and treatment， and the improved particle swarm optimization algorithm is used. The background value is optimized to improve the accuracy of the predicted value； finally， the model is used to predict the amount of medical treatment from 2017 to 2020. The simulation results show that the model has higher accuracy than the single GM（1，N） model， which indicates that the model is effective and feasible. |
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| Keywords: | forecast of diagnosis and treatment volume of medical institutions grey relational analysis GM（1 N） model PSO background value optimization |
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