[1]李怡琳,苏学峰,李慧,等.基于多源数据融合的流行病组合预测方法[J].中国医学物理学杂志,2024,41(2):258-264.[doi:DOI:10.3969/j.issn.1005-202X.2024.02.021]
　LI Yilin,SU Xuefeng,LI Hui,et al.Epidemic prediction method based on multi-source data fusion[J].Chinese Journal of Medical Physics,2024,41(2):258-264.[doi:DOI:10.3969/j.issn.1005-202X.2024.02.021]
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基于多源数据融合的流行病组合预测方法()
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《中国医学物理学杂志》[ISSN:1005-202X/CN:44-1351/R]

卷:
41卷
期数:
2024年第2期
页码:
258-264
栏目:
其他（激光医学等）
出版日期:
2024-03-13

文章信息/Info

Title:
Epidemic prediction method based on multi-source data fusion
文章编号:
1005-202X（2024）02-0258-07
作者:
李怡琳1; 苏学峰2; 李慧3; 朱梦旎4
1.山西医科大学公共卫生学院， 山西 太原 030000； 2.山西省临汾市人民医院院办公室， 山西 临汾 041000； 3.山西省临汾市人民医院智慧医院办公室， 山西 临汾040000； 4.山西医科大学管理学院， 山西 太原 030000
Author(s):
LI Yilin1;  SU Xuefeng2;  LI Hui3;  ZHU Mengni4
1. School of Public Health, Shanxi Medical University, Taiyuan 030000, China 2. Hospital Office, Linfen Peoples Hospital, Linfen 041000, China 3. Office of Smart Hospital, Linfen Peoples Hospital, Linfen 040000, China 4. School of Management, Shanxi Medical University, Taiyuan 030000, China
关键词:
流行病; 预测模型; 多源数据融合; 组合预测; 发病趋势
Keywords:
Keywords: epidemic prediction model multi-source data fusion combined prediction incidence trend
分类号:
R318；TP391
DOI:
DOI:10.3969/j.issn.1005-202X.2024.02.021
文献标志码:
A
摘要:
为了改善传统流行病预测模型普遍存在的准确度低、泛化性弱、结构单一、非线性处理能力差、预测时间长等问题，研究基于多源数据融合的流行病组合预测方法。将采集到的流行病多源数据进行归一化和主成分分析技术特征选择，结合ARIMA模型、灰色GM模型和BPNN神经网络，构建一个ARIMA-GM-BPNN流行病组合预测模型，将前两种预测模型的拟合值作为BPNN神经网络的输入，进行模型训练，将数据充分融合后，综合不同预测模型的优点，获取最优流行病组合预测模型，对未来流行病的发病率和发病趋势进行预测。实验表明该方法组合模型拟合效果很好，流行病发病率预测值与真实值接近，预测趋势也十分吻合，提高预测精度和泛化能力，可以为流行病的预测和防治工作提供可靠的数据支持。
Abstract:
Abstract: A combined epidemic prediction method based on multi-source data fusion is presented to address the common problems of low accuracy, weak generalization, single structure, poor nonlinear processing ability, and long prediction time in traditional epidemic prediction models. The collected multi-source epidemic data are normalized and subjected to feature selection using principal component analysis. An ARIMA-GM-BPNN model for pandemic prediction is constructed by combining ARIMA model, grey GM model and BPNN. The fitting values of the first two prediction models are used as inputs to BPNN for model training. After sufficiently integrating the data and combining the advantages of different prediction models, the optimal combined model is obtained and used for forecasting the incidence and trend of epidemics. Experimental results show that the combined model exhibits excellent fitting performance, with predicted incidences and trends consistent with the real conditions. The proposed approach improves prediction accuracy and generalization capabilities, and it can provide reliable data support for epidemic prediction and control.

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备注/Memo

备注/Memo:
【收稿日期】2023-10-28 【基金项目】山西省重点研发计划（201903D311010） 【作者简介】李怡琳，在读研究生，研究方向：公共卫生，E-mail: L1104598000@163.com
更新日期/Last Update: 2024-02-27