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数据分析与知识发现  2018, Vol. 2 Issue (8): 10-15     https://doi.org/10.11925/infotech.2096-3467.2018.0205
  研究论文 本期目录 | 过刊浏览 | 高级检索 |
基于BRFSS数据库应用人工神经网络构建儿童哮喘预测模型*
马晓宇1,2, 张晗1, 赵玉虹1,2()
1中国医科大学医学信息学院 沈阳 110122
2中国医科大学附属盛京医院临床流行病学教研室 沈阳 110004
Building Childhood Asthma Prediction Model with Artificial Neural Network and BRFSS Database
Ma Xiaoyu1,2, Zhang Han1, Zhao Yuhong1,2()
1Department of Medical Informatics, China Medical University, Shenyang 110122, China
2Department of Clinical Epidemiology, Shengjing Hospital of China Medical University, Shenyang 110004, China
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摘要 

目的】利用BRFSS数据库, 找出对儿童哮喘影响较大的高相关变量, 建立简单易行、无需侵入性临床指标的儿童哮喘预测模型。【方法】采用统计学方法对变量进行筛选, 利用BP人工神经网络的方法建立预测模型, 并与传统Logistic回归、决策树及支持向量机方法所建模型进行比较。【结果】最终纳入预测模型的变量共4项, 包括哮喘史、吸入器使用是否正确、确诊年龄、家庭收入。BP人工神经网络建立的预测模型准确度达0.723, 灵敏度达0.697, 特异度达0.680。【局限】BRFSS数据库属回访型调查, 数据存在缺失, 一定程度上会影响预测效果。【结论】BP人工神经网络建立的儿童哮喘最优预测模型对影响因素多且关系复杂的哮喘疾病, 更能发挥其自适应强的优点。

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马晓宇
张晗
赵玉虹
关键词 BP人工神经网络儿童哮喘预测模型    
Abstract

[Objective] This study tries to identify high-correlated variables with significant impacts on childhood asthma, aiming to establish predictive model without invasive clinical indicators. [Methods] First, we used statistical methods to identify the needed variables from the BRFSS database. Second, we employed the back propagation artificial neural network to build the prediction model. Finally, we compared the performance of the new model with three other methods: the traditional logistic regression, decision tree and support vector machine. [Results] The identified variables included history of asthma, correct use of inhaler, age of diagnosis, and family income. The proposed model has an accuracy of 0.723, a sensitivity of 0.697 and a specificity of 0.680. [Limitations] The BRFSS database has lots of missing data, which may influence the prediction accuracy. [Conclusions] The self-adaptable BP artificial neural network, could help us establish better prediction models for childhood asthma.

Key wordsBack Propagation    Artificial Neural Network    Childhood Asthma    Prediction Model
收稿日期: 2018-02-26      出版日期: 2018-09-08
ZTFLH:  R725.6 G35  
基金资助:*本文系2017年度国家重点研发计划“精准医学”重点专项基金项目“东北区域自然人群队列研究”(项目编号: 2017YFC0907400)的研究成果之一
引用本文:   
马晓宇, 张晗, 赵玉虹. 基于BRFSS数据库应用人工神经网络构建儿童哮喘预测模型*[J]. 数据分析与知识发现, 2018, 2(8): 10-15.
Ma Xiaoyu,Zhang Han,Zhao Yuhong. Building Childhood Asthma Prediction Model with Artificial Neural Network and BRFSS Database. Data Analysis and Knowledge Discovery, 2018, 2(8): 10-15.
链接本文:  
https://manu44.magtech.com.cn/Jwk_infotech_wk3/CN/10.11925/infotech.2096-3467.2018.0205      或      https://manu44.magtech.com.cn/Jwk_infotech_wk3/CN/Y2018/V2/I8/10
  基于BRFSS数据库建立儿童哮喘预测模型的研究流程
数据类型 变量 ACBS中所示名称 类数/数值范围
分类型 孩子性别 RCSGENDR 2
孩子种族 @_RACE 4
医生是否教过吸入器的使用 INHALERH 3
孩子有无保险 INS_TYP 2
家长有无保险 INS1 2
被动吸烟 SMOKE 2
家长受教育程度 @_EDUCAG 3
家庭收入 @_INCOMG 6
家长有无心脏病(或心肌梗塞) CVDINFR4 2
家长有无肾病 CHCKIDNY 2
家长有无糖尿病 DIABETE3 2
家长有无慢阻肺、慢性支气管炎 CHCCOPD1 2
家长有无关节炎 HAVARTH3 2
家长有无抑郁 ADDEPEV2 2
家长有无哮喘 ASTHMA3 2
数值型 孩子确诊年龄 AGEDX 0-3
孩子出生月份 BRTHMNTH 1-12
孩子出生体重 BIRTHW1 0.7-12
母亲生产时年龄 AGEM 9-65
  儿童哮喘预测指标信息
变量名 B S.E. Exp(B) 95%Exp(B) Sig.
下限 上限
ASTHMA3 -0.814 0.220 0.443 0.288 0.682 0.000
INHALERH -0.263 0.040 0.769 0.711 0.831 0.000
AGEDX 0.159 0.070 1.173 1.022 1.345 0.023
@_INCOMG -0.094 0.042 0.910 0.839 0.988 0.025
  Logistic回归变量筛选结果
方法 准确度 灵敏度 特异度
BP人工神经网络 0.723 0.697 0.680
Logistic回归 0.702 0.712 0.492
决策树 0.691 0.708 0.545
支持向量机 0.696 0.712 0.523
  4种不同算法所构建模型预测性能比较
[1] Valet R S, Gebretsadik T, Carroll K N, et al.High Asthma Prevalence and Increased Morbidity Among Rural Children in a Medicaid Cohort[J]. Annals of Allergy Asthma & Immunology, 2011, 106(6): 467-473.
[2] GINA Global Strategy for Asthma Management and Prevention updated 2017[R/OL]. [2017-12-25]. .
[3] Caudri D, Wijga A, Schipper C M A, et al. Predicting the Long-Term Prognosis of Children with Symptoms Suggestive of Asthma at Preschool Age[J]. The Journal of Allergy Clinical Immunology, 2009, 124(5): 903-910.
doi: 10.1016/j.jaci.2009.06.045
[4] Smit H A, Pinart M, Antó J M, et al.Childhood Asthma Prediction Models: A Systematic Review[J]. Lancet Respiratory Medicine, 2015, 3(12): 973-984.
doi: 10.1016/S2213-2600(15)00428-2 pmid: 26597131
[5] Behavioral Risk Factor Surveillance System [DB/OL]. [2017-11-28]..
[6] Castro-Rodríguez J A, Holberg C J, Wright A L, et al. A Clinical Index to Define Risk of Asthma in Young Children with Recurrent Wheezing[J]. American Journal of Respiratory and Critical Care Medicine, 2000, 162(4): 1403-1406.
doi: 10.1164/ajrccm.162.4.9912111 pmid: 11029352
[7] Brand P L.The Asthma Predictive Index: Not a Useful Tool in Clinical Practice[J]. The Journal of Allergy Clinical Immunology, 2011, 127(1): 293-294.
doi: 10.1016/j.jaci.2010.10.012 pmid: 21075441
[8] Van Der Mark L B, Van Wonderen K E, Mohrs J, et al. Predicting Asthma in Preschool Children at High Risk Presenting in Primary Care: Development of a Clinical Asthma Prediction Score[J]. Primary Care Respiratory Journal, 2014, 23(1): 52-59.
doi: 10.4104/pcrj.2014.00003
[9] Chatzimichail E, Paraskakis E, Rigas A.An Evolutionary Two-Objective Genetic Algorithm for Asthma Prediction[C]// Proceedings of 2013 UKSim 15th International Conference on Computer Modelling and Simulation, Cambridge, United Kingdom. US: IEEE, 2013.
[10] 刘苗苗, 王达, 任万辉, 等. 家养皮毛宠物与儿童哮喘关系[J]. 中国公共卫生, 2012, 28(11): 1420-1430.
doi: 10.11847/zgggws2012-28-11-11
[10] (Liu Miaomiao, Wang Da, Ren Wanhui, et al.Relationship Between Pet Keeping and Childhood Asthma in Shenyang City[J]. Chinese Journal of Public Health, 2012, 28(11): 1420-1430.)
doi: 10.11847/zgggws2012-28-11-11
[11] 周燕凤. 支气管哮喘发病危险因素分析及相关护理对策[J]. 当代医学, 2011, 17(27): 133-134.
doi: 10.3969/j.issn.1009-4393.2011.27.094
[11] (Zhou Yanfeng.Risk Factors of Bronchial Asthma and Related Nursing Strategies[J]. Contemporary Medicine, 2011, 17(27): 133-134.)
doi: 10.3969/j.issn.1009-4393.2011.27.094
[12] Finkelstein J, Jeong I C.Machine Learning Approaches to Personalize Early Prediction of Asthma Exacerbations[J]. Annals of the New York Academy of Sciences, 2017, 1387(1): 153-165.
doi: 10.1111/nyas.13218 pmid: 27627195
[13] Kim W, Kim K S, Lee J E, et al.Development of Novel Breast Cancer Recurrence Prediction Model Using Support Vector Machine[J]. Journal of Breast Cancer, 2012, 15(2): 230-238.
doi: 10.4048/jbc.2012.15.2.230 pmid: 22807942
[14] 牟冬梅, 任珂. 三种数据挖掘算法在电子病历知识发现中的比较[J]. 现代图书情报技术, 2016(6): 102-109.
[14] (Mu Dongmei, Ren Ke.Discovering Knowledge from Electronic Medical Records with Three Data Mining Algorithms[J]. New Technology of Library and Information Service, 2016(6): 102-109.)
[15] 李丽霞, 张敏, 郜艳晖, 等. 人工神经网络在医学研究中的应用[J]. 数理医药学杂志, 2009, 22(1): 80-82.
[15] (Li Lixia, Zhang Min, Gao Yanhui, et al.Application of Artificial Neural Network in Medical Research[J]. Journal of Mathematical Medicine, 2009, 22(1): 80-82.)
[16] Bisgaard H, Szefler S.Prevalence of Asthma-Like Symptoms in Young Children[J]. Pediatric Pulmonology, 2007, 42(8): 723-728.
doi: 10.1002/ppul.v42:8
[17] Savenije O E, Granell R, Caudri D, et al.Comparison of Childhood Wheezing Phenotypes in 2 Birth Cohorts: ALSPAC and PIAMA[J]. The Journal of Allergy Clinical Immunology, 2011, 127(6): 1505-1512.
doi: 10.1016/j.jaci.2011.02.002 pmid: 21411131
[18] Caudri D, Wijga A, Maarten C, et al.Predicting the Long-Term Prognosis of Children with Symptoms Suggestive of Asthma at Preschool Age[J]. The Journal of Allergy Clinical Immunology, 2009, 124(5): 903-910.
doi: 10.1016/j.jaci.2009.06.045
[19] 袁梅宇. 数据挖掘与机器学习—WEKA应用技术与实践[M]. 北京: 清华大学出版社, 2014.
[19] (Yuan Meiyu.Data Mining and Machine Learning — WEKA Application Technology and Practice[M]. Beijing: Tsinghua University Press, 2014.)
[20] Kumar Y, Sahoo G.Prediction of Different Types of Liver Diseases Using Rule Based Classification Model[J]. Technology & Health Care, 2013, 21(5): 417-432.
doi: 10.3233/THC-130742 pmid: 23963359
[21] 方积乾. 现代医学统计学[M]. 北京: 人民卫生出版社, 2002: 708-718.
[21] (Fang Jiqian.Advanced Medical Statistics[M]. Beijing: People’s Medical Publishing House, 2002: 708-718.)
[22] Rumelhart D E, Hinton G E, Williams G J, et al.Learning Internal Representation by Back-Propagation Errors[J]. Nature, 1986, 323: 533-536.
doi: 10.1038/323533a0
[23] Dayhoff J E, Deleo J M.Artificial Neural Networks[J]. Cancer, 2001, 91(8): 1615-1634.
doi: 10.1002/(ISSN)1097-0142
[24] Cross S S, Harrison R F, Kennedy R L.Introduction to Neural Networks[J]. Lancet, 1995, 346(8982): 1075-1079.
doi: 10.1016/S0140-6736(95)91746-2
[25] 张良均, 王路, 谭立云, 等. Python数据分析与挖掘实战[M]. 北京: 机械工业出版社, 2015.
[25] (Zhang Liangjun, Wang Lu, Tan Liyun, et al.Python Practice of Data Analysis and Mining[M]. Beijing: China Machine Press, 2015.)
[26] 孙振球. 医学统计学[M]. 北京: 人民卫生出版社, 2007: 333-341.
[26] (Sun Zhenqiu.Medical Statistics[M]. Beijing: People’s Medical Publishing House, 2007: 333-341.)
[27] 张良均, 曹晶, 蔡世忠. 神经网络实用教程[M]. 北京: 机械工业出版社, 2008:31-36.
[27] (Zhang Liangjun, Cao Jing, Cai Shizhong.Neural Network Practical Guide[M]. Beijing: China Machine Press, 2008: 31-36.)
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