中国组织工程研究

中国组织工程研究 ›› 2011, Vol. 15 ›› Issue (9): 1702-1705.doi: 10.3969/j.issn.1673-8225.2011.09.044

• 数字化骨科 digital orthopedics • 上一篇    下一篇

BP神经网络在疾病分析影响因素中的作用

周金海，申刚磊，丁小丽，杨  涛   

  1. 南京中医药大学信息技术学院，江苏省南京市  210046
  • 收稿日期:2010-10-08 修回日期:2010-12-06 出版日期:2011-02-26 发布日期:2011-02-26
  • 作者简介:周金海，1958年生，副教授，高级工程师，硕士生导师，主要从事医药信息工程﹑人工智能方面的研究。
  • 基金资助:

    江苏省高校自然科学研究计划项目(08KJD520020)，南京中医药大学医史文献国家重点学科全国招标项目(2007NO22)。

BP neural network in analysis of disease influential factors

Zhou Jin-hai, Shen Gang-lei, Ding Xiao-li, Yang Tao   

  1. Institute of Information Technology, Nanjing University of Chinese Medicine, Nanjing  210046, Jiangsu Province, China
  • Received:2010-10-08 Revised:2010-12-06 Online:2011-02-26 Published:2011-02-26
  • About author:Zhou Jin-hai, Associate professor, Senior engineer, Master’s supervisor, Institute of Information Technology, Nanjing University of Chinese Medicine, Nanjing 210046, Jiangsu Province, China zhoujh2003@126.com
  • Supported by:

    the Natural Science Foundation of Universities of Jiangsu Province, No. 08KJD520020*; the National Key Subjects of Traditional Chinese Medicine Medical History of Nanjing University of Chinese Medicine, No. 2007NO22*

PDF

549

被引次数

11

摘要：

背景：疾病产生原因复杂多样，临床医生对大量样本病历数据挖掘的探讨往往缺乏有效的手段，信息技术的应用能力有待提高。
目的：利用人工神经网络的BP算法，对临床大样本量的病历进行分析，以找出某种疾病的致病因素与疾病本身之间的内在关系。
方法：以高血压病为例，以某中医院2010-07的高血压患者病历数据为实验数据，对疾病的影响因素进行建模，优选 Microsoft SQL Server 2005 Analysis Services智能工具，分析其挖掘结果，并利用单独查询进行预测与决策支持。
结果与结论：应用基于BP算法的人工神经网络分析疾病的致病因素对疾病本身的影响有较好的预测效果，有利于提升医务人员借助信息技术方法在临床诊断的水平，提高疾病诊断效率。

关键词: 人工神经网络, BP算法, 高血压病, 医学数据挖掘, 人工智能, BP神经网络

Abstract:

BACKGROUND: Disease pathogenic factors are complicated. There is not an effective method to analyze large sample data mining, and application ability of information technology of clinical doctors needs to be improved. 
OBJECTIVE: Using BP algorithm of artificial neural network to analyze large sample clinical cases, in order to explore inner relations between disease pathogenic factors and diseases.
METHODS: Take hypertension for example, medical data of patients with hypertension in a traditional Chinese medical hospital served as experimental data, and the influence factors of the disease were simulated with Microsoft SQL Server 2005 Analysis Services, the mining data was analyzed, and a single query was used as prediction and decision support.
RESULTS AND CONCLUSION: Analysis of effect of disease pathogenic factors on disease itself based on artificial neural network with BP algorithm has good predictive effect in clinical diagnosis, which is of benefit to enhance the diagnostic efficiency of medical personnel using information technology.

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