主办单位:中国气象局沈阳大气环境研究所
国际刊号:ISSN 1673-503X
国内刊号:CN 21-1531/P

气象与环境学报 ›› 2018, Vol. 34 ›› Issue (4): 126-133.doi: 10.3969/j.issn.1673-503X.2018.04.017

• 论文 • 上一篇    下一篇

多种气象统计模型对比研究——以气象敏感性疾病脑卒中预报为例

刘博1,2 党冰2,3 张楠1 王式功2,4 尹岭5 张晓云6 黎檀实5 卢震华7   

  1. 1. 天津市气象台,天津 300074;2. 兰州大学大气科学学院甘肃省干旱气候变化与减灾重点实验室,甘肃 兰州 730000;3. 北京市气候中心,北京 100089;4. 成都信息工程大学大气科学学院,四川 成都 610103;5. 中国人民解放军总医院,北京 100853;6. 天津市气象科学研究所,天津 300074;7. 兰州大学第一临床医学院,甘肃 兰州 730000
  • 收稿日期:2017-04-01 修回日期:2017-05-31 出版日期:2018-08-31 发布日期:2018-09-03

Comparison of various meteorological statistical forecasting models-Taking causing- stroke weather forecasting as an example

LIU Bo1,2  DANG Bing2,3   ZHANG Nan  WANG Shi-gong2,4  YIN Ling5  ZHANG Xiao-yun6  LI Tan-shi5  LU Zhen-hua7   

  1. 1. Tianjin Meteorological Observatory, Tianjin 300074, China; 2. College of Atmosphere Science, Lanzhou University, Key Laboratory of Arid Climatic Change and Disaster Mitigation of Gansu Province, Lanzhou 730000, China; 3. Beijing Municipal Climate Center, Beijing 100089, China; 4. School of Atmospheric Science, Chengdu University of Information Technology, Chengdu 610103, China; 5. General Hospital of PLA, Beijing 100853, China; 6. Tianjin Institute of Meteorological Science, Tianjin 300074,China; 7. The First Clinical Medical College of Lanzhou University, Lanzhou 730000,China
  • Received:2017-04-01 Revised:2017-05-31 Online:2018-08-31 Published:2018-09-03

摘要:

基于2008—2012年北京市4家三甲医院的脑卒中疾病急诊就诊资料及同期气象观测资料和环境监测资料,筛选气象和环境预报因子,采用广义相加(Generalized Additive Model,GAM)、逐步回归、BP(Back Propagation)神经网络及决策树4种方法编辑数据训练集(2008—2011年)和验证集(2012年)输入模型,建立北京市脑卒中疾病预报模型,计算各模型的拟合优度和预报准确率,对比分析脑卒中疾病各预报模型并确定最优预报方法。结果表明:北京市四季脑卒中疾病不同模型选取的预报因子不同,其中时间序列为重要的预报因子。GAM模型对高等级脑卒中疾病的预报效果最好,逐步回归模型对中间等级脑卒中疾病的预报效果最好,决策树模型对低等级脑卒中疾病的预报效果最好。4种脑卒中疾病预报模型四季平均的预报准确率依次为:GAM>神经网络模型>逐步回归模型>决策树。GAM模型脑卒中疾病的平均和高等级预报准确率均为最高,其中出血性脑卒中预报模型的完全预报准确率为68.3%,预报误差≤±1级的准确率达98.0%,可以满足天气变化对出血性脑卒中疾病预报预警的业务需求。

关键词: 医疗气象, 脑卒中疾病, 广义相加模型GAM, BP神经网络, 决策树

Abstract:

 Based on the data of stroke emergency visits from four hospitals in Beijing during 2008 to 2012, as well as the observed daily meteorological and environmental factors, meteorological and environmental predictors were selected. All the data were divided into two groups, that is, calibration set (2008-2011) and validation set (2012) . Causing-stroke weather forecasting model was constructed using four methods including Stepwise Regression Model (SRM), BP Neural Network Model, Decision Tree Model (DTM) and Generalized Additive Model (GAM). The best model for forecasting the number of stroke patients was determined by comparing the goodness of fit and forecasting accuracy of different models. The results show that the selected predictors vary are varied along with different seasons and models.  and The time series factor is the most important factorindispensable. GAM produces , SRM, and DTM methods give the best performance in at forecasting number of the high-level-stoke., SRM gives the best result for the prediction of the medium-level-stroke.  andDTM provides the best estimate for the low-level-stroke patients, respectively.. The sequence of averaged forecasting accuracy over the four seasons from of the three different models is as follows: GAM>BP Neural Network Model>SRM>DTM. GAM has the highest forecasting accuracy for the number of averaged and high–level-stroke patients . For, the cerebral hemorrhagic stroke (CHS), the forecasting accuracy is 68.3% if the forecasted grade is exactly correct. For the bias grade  ≤±1difference between forecasted grade and actual grade no more than 1, the forecasting accuracy is 98%. The result indicates  reveals that GAM can basically meet the demand for medical meteorological forecasting of CHS.

Key words: Medical meteorology, Stroke, Generalized Additive Model (GAM), BP neural network, Decision tree method

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