文章摘要
韩家信,熊鸿燕,张廷惠,许斌,李亚斐,朱才众,马翔宇,张路.炭疽病的诊断及危险度预测智能模型研究[J].中华流行病学杂志,2006,27(10):875-879
炭疽病的诊断及危险度预测智能模型研究
Development of a model for the diagnosis and risk classification on anthrax through artificial neural network
收稿日期:2006-04-07  出版日期:2014-10-17
DOI:
中文关键词: 炭疽;神经网络(计算机);流行病学
英文关键词: Anthrax;Neural network(computer);Epidemiology
基金项目:全军军事科研“十五”计划课题资助项目(05QJ238-017)
作者单位E-mail
韩家信 第三军医大学预防医学院流行病学教研室  
熊鸿燕 第三军医大学预防医学院流行病学教研室 hyxiong@mail.tmmu.com.cn 
张廷惠 重庆市药剂学校卫生学教研室  
许斌 第三军医大学预防医学院流行病学教研室  
李亚斐 第三军医大学预防医学院流行病学教研室  
朱才众 第三军医大学预防医学院流行病学教研室  
马翔宇 第三军医大学预防医学院流行病学教研室  
张路 第三军医大学预防医学院流行病学教研室  
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中文摘要:
      目的建立以临床和流行病学指标为基本分析因子的综合诊断及预测炭疽危害程度的智能预测模型,提高对炭疽病发生的认识和判断能力。方法根据实际疾病案例资料,分析临床症状、实验室检测指标、流行病学特征等因素。选入明显影响炭疽诊断和流行强度的指标,并将其作为神经元单位。利用Matlab 6.1软件中的神经网络工具箱训练、调整和建立智能化分析系统。结果多因素相关分析显示,疾病潜伏期、胸部X线检验结果>、镜检结果、职业特征等11项指标与炭疽病的诊断和流行强度有关;神经网络经500步学习和训练,训练误差从6.669 59下降至5.05119×10-11,通过建立的智能神经网络模型对炭疽和非炭疽实际案例进行诊断和预测分析,其平均符合率达到100%。结论人工神经网络在疾病综合特征与炭疽诊断和危害度预测之间建模是可行的,所训练的智能模型预测平均符合率达100%,有很好的实际应用价值。
英文摘要:
      Objective Based on data through clinical and epidemiological studies,a model regarding the diagnosis and risk classification on anthrax was developed by artificial neural network (ANN). The model could integrally diagnose anthrax cases, judge the risk tendency in time,and increase the ability of recognizing the anthrax accidents. Methods Clinical, laboratory and epidemiological data from anthrax cases was collected and analyzed. The important factors which could greatly influence the results on diagnosis and judgment was chosen and used as the neural units. Through the use of artificial neural network analytic method (back propagation,BP),an intelligent model on the diagnosis and risk classification was developed. Results Results from the multivariate analysis revealed that: 11 factors including incubation period, chest radiographic and microscopic findings, characteristics on professions etc. were associated with the judgment on the diagnosis and intensity of the epidemics. Through 500 times training on the neural network, the performance error decreased from 6.669 59 to 5.051 19×10-11. The model was then validated. With 100% average correct rate, the predictive value was good. Conclusion It was feasible to use the disease information to develop a diagnosis and risk classification model on anthrax by artificial neural network. With 100% average correct rate,the established model was valuable in practice.
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