An efficient machine learning approach for diagnosis of paraquat-poisoned patients

doi:10.1016/j.compbiomed.2015.02.003

科研成果详情

题名	An efficient machine learning approach for diagnosis of paraquat-poisoned patients
作者	Hu, Lufeng1; Hong, Guangliang2; Ma, Jianshe3; Wang, Xianqin3; Chen, Huiling 4
发表日期	2015-04-01
发表期刊	COMPUTERS IN BIOLOGY AND MEDICINE 影响因子和分区
语种	英语
原始文献类型	Article
关键词	Paraquat Poison Extreme learning machine Medical diagnosis
其他关键词	FEEDFORWARD NETWORKS ; BIAS CORRECTION ; CLASSIFICATION
摘要	Numerous people die of paraquat (PQ) poisoning because they were not diagnosed and treated promptly at an early stage. Till now, determination of PQ levels in blood or urine is still the only way to confirm the PQ poisoning. In order to develop a new diagnostic method, the potential of machine learning technique was explored in this study. A newly,developed classification technique, extreme learning machine (ELM), was taken to discriminate the PQ-poisoned patients from the healthy controls. 15 PQ-poisoned patients recruited from The First Affiliated Hospital of Wenzhou Medical University who had a history of direct contact with PQ and 16 healthy volunteers were involved in the study. The ELM method is examined based on the metabolites of blood samples determined by gas chromatography coupled with mass spectrometry in terms of classification accuracy, sensitivity, specificity and AUC (area under the receiver operating characteristic (ROC) curve) criterion, respectively. Additionally, the feature selection was also investigated to further boost the performance of ELM and the most influential feature was detected. The experimental results demonstrate that the proposed approach can be regarded as a success with the excellent classification accuracy, AUC, sensitivity and specificity of 91.64%, 0.9156%, 9133% and 91.78%, respectively. Promisingly, the proposed method might serve as a new candidate of powerful tools for diagnosis of PQ-poisoned patients with excellent performance. (C) 2015 Elsevier Ltd. All rights reserved.
资助项目	National Natural Science Foundation of China (NSFC)National Natural Science Foundation of China (NSFC) [61303113, 81401558, 61402337]; Science and Technology Committee of Shanghai Municipality of China [KF1405]; Zhejiang Provincial Natural Science Foundation of ChinaNatural Science Foundation of Zhejiang Province [LY14H230001, LQ13G010007, LQ13F020011, LY14F020035]; key construction academic subject (medical innovation) of Zhejiang Province [11-CX26]
出版者	PERGAMON-ELSEVIER SCIENCE LTD
出版地	OXFORD
ISSN	0010-4825
EISSN	1879-0534
卷号	59 页码:116-124
DOI	10.1016/j.compbiomed.2015.02.003
页数	28
WOS类目	Biology ; Computer Science, Interdisciplinary Applications ; Engineering, Biomedical ; Mathematical & Computational Biology
WOS研究方向	Life Sciences & Biomedicine - Other Topics ; Computer Science ; Engineering ; Mathematical & Computational Biology
WOS记录号	WOS:000352172600014
收录类别	SCIE ; PUBMED ; EI ; SCOPUS
EI入藏号	20150800551283
EI主题词	Diagnosis
URL	查看原文
PubMed ID	25704654
SCOPUSEID	2-s2.0-84923046261
通讯作者地址	[Chen, Huiling]College of Physics and Electronic Information Engineering, Wenzhou University,Wenzhou,325035,China
Scopus学科分类	Computer Science Applications;Health Informatics
TOP期刊	TOP期刊
引用统计	被引频次：122[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	https://kms.wmu.edu.cn/handle/3ETUA0LF/18346
专题	附属第一医院基础医学院（机能实验教学中心）第一临床医学院（信息与工程学院）、附属第一医院基础医学院（机能实验教学中心）_基础医学实验教学中心_机能实验教学中心（挂靠、校级）
通讯作者	Chen, Huiling
作者单位	1.The First Affiliated Hospital of Wenzhou Medical University,Wenzhou,325000,China; 2.Department of Emergency, The First Affiliated Hospital of Wenzhou Medical University,Wenzhou,325000,China; 3.Function Experiment Teaching Center, Wenzhou Medical University,Wenzhou,325035,China; 4.College of Physics and Electronic Information Engineering, Wenzhou University,Wenzhou,325035,China
第一作者单位	附属第一医院; 第一临床医学院（信息与工程学院）、附属第一医院
第一作者的第一单位	附属第一医院
推荐引用方式 GB/T 7714	Hu, Lufeng,Hong, Guangliang,Ma, Jianshe,et al. An efficient machine learning approach for diagnosis of paraquat-poisoned patients[J]. COMPUTERS IN BIOLOGY AND MEDICINE,2015,59:116-124.
APA	Hu, Lufeng, Hong, Guangliang, Ma, Jianshe, Wang, Xianqin, & Chen, Huiling. (2015). An efficient machine learning approach for diagnosis of paraquat-poisoned patients. COMPUTERS IN BIOLOGY AND MEDICINE, 59, 116-124.
MLA	Hu, Lufeng,et al."An efficient machine learning approach for diagnosis of paraquat-poisoned patients".COMPUTERS IN BIOLOGY AND MEDICINE 59(2015):116-124.