Research and Development of Intelligent Q&A System for Medical Industry

LIU Fang; YU Fei

Chinese

您当前的位置：

首页 >

文章列表页 >

Research and Development of Intelligent Q&A System for Medical Industry

更新时间：2026-09-04

- Research and Development of Intelligent Q&A System for Medical Industry
- Vol. 29, Issue 11, Pages: 95-98(2012)
- 作者机构：
  
  扬州市职业大学工商管理学院,江苏,扬州,225009
- 作者简介：
- 基金信息：
- DOI：
  CLC：
- Published：2012
- 稿件说明：
移动端阅览
刘芳, 于斐. 面向医疗行业的智能问答系统研究与实现[J]. 微电子学与计算机, 2012, 29(11): 95-98.

LIU Fang, YU Fei. Research and Development of Intelligent Q&A System for Medical Industry[J]. 2012, 29(11): 95-98.
刘芳, 于斐. 面向医疗行业的智能问答系统研究与实现[J]. 微电子学与计算机, 2012, 29(11): 95-98. DOI：

LIU Fang, YU Fei. Research and Development of Intelligent Q&A System for Medical Industry[J]. 2012, 29(11): 95-98. DOI：

摘要

为了实现医疗信息化中的疾病与诊疗的智能问答系统

本文提出一种基于人机交互的用户自然语言问题理解方法.该方法在关键词库的基础上

采用正向最大匹配算法对用户咨询进行词法分析.进而对医疗领域的问题进行分类

系统根据问题类别进行数据检索;并利用模板匹配方法

完成答案的提取

以句子作为答案的基本单位.最后

在JSP和Java平台上实现了B/S模式的医疗领域智能问答系统

验证了该方法的有效性.

Abstract

In order to realize the intelligent question answering system for disease diagnosis and treatment in medical informationization

this article proposed a method for understanding the userquestion described by nature language based on human-computer interaction.On the basis of the Keywords library

the method use the maximum matching algorithm for lexical analysing of the user consulting.And then classify the issues of medical field

the system take the data retrieval according to the categories of issues.The system will also use the method of template matching for extracting the answer

with the sentence as the basic unit of answer.Finally

intelligent question answering system of medical field is realized based on B/S model on the platform of JSP and JAVA

it prove the effectiveness of this method.

关键词

Keywords

references

Views

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

A high-efficiency charge pump for ultra-low voltage boost systems

Aerial small object detection algorithm based on improved RT-DETR

Monte carlo analysis of V-layout trench SiC neutron detector

A review of large model development: technological evolution from BERT to Deepseek

Microservices anomaly detection based on the correlation of monitoring metrics

Related Author

No data

Related Institution

No data

AI问答

Postal code：100079
Tel：（010）53879206 Email：tmw@bjxintong.com.cn
Technical support is provided by Beijing Founder electronics co., LTD 京ICP备09082226号-64 京公网安备11010602201714号
It is recommended to read the content of this site in Chrome&IE9+. Please switch to extreme mode in browser 360.
Cookies We use cookies to help provide and enhance our service and tailor content. By continuing, you agree to the use of cookies.

⁰