中国组织工程研究

中国组织工程研究 ›› 2011, Vol. 15 ›› Issue (35): 6592-6595.doi: 10.3969/j.issn.1673-8225.2011.35.031

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

基于BP人工神经网络模型构建电子病历系统的数据分析

王欣萍1，孙  昕1，孙  尧2   

  1. 1哈尔滨医科大学基础医学院计算机教研室，黑龙江省哈尔滨市  150081
    2 黑龙江中医药大学信息中心计算机教研室，黑龙江省哈尔滨市  150081
  • 收稿日期:2011-04-11 修回日期:2011-07-07 出版日期:2011-08-27 发布日期:2011-08-27
  • 通讯作者: 孙昕，硕士，讲师，哈尔滨医科大学基础医学院计算机教研室，黑龙江省哈尔滨市 150081 sunxinamity@163.com
  • 作者简介:王欣萍★，女，1977年生，黑龙江省泰来县人，汉族，2009年哈尔滨工程大学毕业，硕士，讲师，主要从事计算机网络研究。 15545018060@163.com
  • 基金资助:

    黑龙江省卫生厅科研项目(2010-176)，课题名称：基于网络的临床诊疗水平评价与考核系统的研究与开发。

Application of BP artificial neural network in the electronic medical record system

Wang Xin-ping1, Sun Xin1, Sun Yao2   

  1. 1Department of Computer, Basic Medical School, Harbin Medical University, Harbin  150081, Heilongjiang Province, China
    2Department of Computer, Information Center, Heilongjiang University of Chinese Medicine, Harbin  180081, Heilongjiang Province, China
  • Received:2011-04-11 Revised:2011-07-07 Online:2011-08-27 Published:2011-08-27
  • Contact: Sun Xin, Master, Lecturer, Department of Computer, Basic Medical School, Harbin Medical University, Harbin 150081, Heilongjiang Province, China sunxinamity@163.com
  • About author:Wang Xin-ping★, Master, Lecturer, Department of Computer, Basic Medical School, Harbin Medical University, Harbin 150081, Heilongjiang Province, China 15545018060@163.com
  • Supported by:

    the Science and Technology Project of Heilongjiang Health Bureau, No. 2010-176*

PDF

424

被引次数

10

摘要：

背景：电子病历中包含大量能够辅助临床诊断和决策的医疗信息。
目的：利用BP人工神经网络进行电子病历的数据挖掘。
方法：针对BP人工神经网络的原理及算法进行了分析，提出BP人工神经网络模型构建的6个步骤，分别为训练数据集的确定，数据准备，网络模型的建立，进行数据挖掘，评估BP网络得到的结果及预测结果的应用。并分析了BP人工神经网络在电子病历中的相关应用。
结果与结论：利用BP人工神经网络可以对电子病历进行分析预测，查找存在的危险因素。证实BP人工神经网络在电子病历系统数据分析中具有实际应用价值。

关键词: 电子病历系统, BP网络, 人工神经网络, 多层前馈, 数字化医学

Abstract:

BACKGROUND: There are a large number of information in the electronic medical records which can assist medical diagnosis and decision-making;
OBJECTIVE: To perform data mining in electronic medical records based on BP neural network.
METHODS: BP artificial neural network theory and algorithms are analyzed, and there are six steps to build a BP artificial neural network model, including the training data set identification, data preparation, network model, data mining, and evaluating and predicting the results obtained from the BP network. The related applications of the BP artificial neural network in the electronic medical records were analyzed.
RESULTS AND CONCLUSION: BP artificial neural network can be used to analyze and predict the electronic medical records to find the risk factors. BP artificial neural network data analysis has practical application value in the electronic medical records system.

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