摘要
目的:探讨基于电子鼻的社区获得性肺炎(CAP)患者不同病位的口腔呼气的气味图谱特征,为本病的中医诊断提供新的思路和方法。方法:选择201例CAP患者及110名健康者,收集其四诊信息并运用电子鼻采集口腔呼气的气味图谱,分析CAP患者的常见病位证素特点以及其口腔呼气的气味图谱特征。结果:CAP的病位证素出现频数从高到低依次为肺、表、肝、脾;CAP患者与健康者的气味图谱特征辨识:采用KNN聚类方法,其识别准确率最高可达99.19%;CAP患者常见病位的气味图谱特征辨识:采用决策树方法,其识别率最高的是肝组,可达93.03%。结论:运用电子鼻能识别CAP及其不同病位间的口腔气味图谱特征,为该病的临床诊断提供参考。
Objective:To explore the characteristics of the odor response pattern of oral exhalation at different disease locations in patients with community-acquired pneumonia(CAP)based on electronic nose,and to provide new ideas and methods for TCM diagnosis of this disease.Methods:A total of 201 patients with CAP and 110 healthy patients were selected to collect the four diagnostic information and to collect the odor response pattern of oral exhalation by the electronic nose.Results:The common syndrome elements of CAP according to the frequency of occurrence from high to low were lung,surface,liver and spleen.Identification of odor response patter characteristics of patients with CAP and healthy people:the recognition accuracy was up to 99.19%by KNN pattern recognition method;Identification of odor response pattern characteristics of common disease locations in patients with CAP:the highest recognition rate was up to 93.03%in liver group by decision tree pattern recognition method.Conclusion:Electronic nose can be used to identify CAP and its oral odor response pattern between different diseases locations,which provides a reference for the clinical diagnosis of the disease.
作者
周福
连梨梨
张劲松
吴敏
林雪娟
ZHOU Fu;LIAN Li-li;ZHANG Jin-song;WU Min;LIN Xue-juan(Research Base of Traditional Chinese Medicine Syndrome,Fujian University of Traditional Chinese Medicine,Fuzhou 350122,China;Key Laboratory of Traditional Chinese Medicine Health Status Identification of Fujian Province,Fuzhou 350122,China;Fujian Provincial Collaborative Innovation Center for 2011 Chinese Medicine Health Management,Fuzhou 350122,China;Jinjiang Hospital of Traditional Chinese Medicine Affiliated to Fujian University of Traditional Chinese Medicine,Jinjiang 362200,China)
出处
《中华中医药杂志》
CAS
CSCD
北大核心
2019年第12期5954-5956,共3页
China Journal of Traditional Chinese Medicine and Pharmacy
基金
国家自然科学基金项目(No.81373552)
福建省自然科学基金项目(No.2018J01892)
福建省2011中医健康管理协同创新项目(No.JG2017009-协同)
载人航天领域预先研究项目(No.020104).
关键词
社区获得性肺炎
证素
病位
气味图谱
电子鼻
决策树
人工神经网络
K最邻近分类算法
Community-acquired pneumonia
Syndrome elements
Disease location
Odor response pattern
Electronic nose
Decision tree
Artificial neural network
KNN
作者简介
通讯作者:林雪娟,福建省福州市闽侯上街大学城邱阳路1号福建中医药大学中医证研究基地,邮编:350122,电话:0591-22861513,E-mail:lxjfjzy@126.com
