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计算机系统应用:2020,29(6):80-88
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基于改进K-NN和SVM的多学科协作诊疗决策支持系统
(1.黑龙江外国语学院 信息工程系, 哈尔滨 150025;2.普渡大学 机械工程系, 西拉法叶市 IN47906;3.哈尔滨工业大学 计算机科学与技术学院, 哈尔滨 150001)
Multidisciplinary Collaborative Diagnosis and Treatment Decision Support System Based on Improved K-NN and SVM
(1.Department of Information Engineering, Heilongjiang International University, Harbin 150025, China;2.Department of Mechanical Engineering, Purdue University, West lafayette, IN47906, USA;3.School of Computer Science and Technology, Harbin Institute of Technology, Harbin 150001, China)
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投稿时间:2019-11-17    修订日期:2019-12-01
中文摘要: 由于当前的诊疗决策支持系统采用单一学科的决策方法, 导致诊疗精度不高, 获取的数据分类结果准确率较低, 提出并设计一种基于改进K-NN (K-Nearest Neighbour)分类算法和SVM (Support Vector Mechine)的多学科协作诊疗决策支持系统. 在构建系统总体框架的基础上, 对数据库系统模块、人机交互模块和诊疗推理模块进行设计, 其中诊疗推理模块是系统的软件核心, 通过改进K-NN分类算法和SVM建立推理引擎, 在计算机的辅助下, 搜索与患者病症信息相似的医疗案例, 并进行相似度匹配, 根据匹配结果与患者症状集构建一个新的临床案例, 引入CDA (Clinical Document Architecture)概念, 实现改进K-NN分类算法和SVM算法的有效融合, 完成多学科协作诊疗决策. 实验结果表明, 与传统系统相比, 该系统的诊疗决策精度高, 评价指标测试平均值达到95.98%, 分类结果准确率较高, 在该系统辅助下能提高医生诊断正确性, 降低误诊率, 且运算复杂度较低.
Abstract:Because the current diagnosis and treatment decision support system adopts a single subject decision-making method, resulting in low diagnosis and treatment accuracy and low accuracy of the obtained data classification results, a multi-disciplinary collaborative diagnosis and treatment decision support system based on improved K-Nearest Neighbor (K-NN) classification algorithm and Support Vector Mechine (SVM) is proposed and designed. Based on the overall framework of the system, the database system module, human-computer interaction module, and diagnosis and treatment reasoning module are designed. The diagnosis and treatment reasoning module is the software core of the system. The reasoning engine is established by improved K-NN classification algorithm and SVM. With the help of computer, medical cases similar to the patient’s disease information are searched, and similarity matching is carried out. According to the matching results, a new clinical case is constructed based on patient symptom set. The concept of Clinical Document Architecture (CDA) is introduced to realize the effective fusion of improved K-NN classification algorithm and SVM algorithm, and to complete the multi-disciplinary collaborative diagnosis and treatment decision. The experimental results show that, compared with the traditional system, the system has high accuracy in diagnosis and treatment decision-making, the average value of the evaluation index is 95.98%, and the accuracy rate of classification results is high. With the help of the system, it can improve the diagnosis accuracy of doctors and reduce the misdiagnosis rate, and the operation complexity is low.
文章编号:7466     中图分类号:    文献标志码:
基金项目:国家自然科学基金(61803117); 教育部科技发展中心产学研创新基金(2018A01002); 国家科技部创新方法专项(2017IM010500)
引用文本:
李晓峰,王妍玮,李东.基于改进K-NN和SVM的多学科协作诊疗决策支持系统.计算机系统应用,2020,29(6):80-88
LI Xiao-Feng,WANG Yan-Wei,LI Dong.Multidisciplinary Collaborative Diagnosis and Treatment Decision Support System Based on Improved K-NN and SVM.COMPUTER SYSTEMS APPLICATIONS,2020,29(6):80-88

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