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Raman spectroscopy for medical diagnostics--From in-vitro biofluid assays to in-vivo cancer detection.拉曼光谱在医学诊断中的应用——从体外生物流体分析到体内癌症检测。
Adv Drug Deliv Rev. 2015 Jul 15;89:121-34. doi: 10.1016/j.addr.2015.03.009. Epub 2015 Mar 22.
2
Raman spectroscopy of blood serum for Alzheimer's disease diagnostics: specificity relative to other types of dementia.用于阿尔茨海默病诊断的血清拉曼光谱:相对于其他类型痴呆症的特异性。
J Biophotonics. 2015 Jul;8(7):584-96. doi: 10.1002/jbio.201400060. Epub 2014 Sep 25.
3
Statistically quantified measurement of an Alzheimer's marker by surface-enhanced Raman scattering.通过表面增强拉曼散射对阿尔茨海默氏症标志物进行统计量化测量。
J Biophotonics. 2015 Jul;8(7):567-74. doi: 10.1002/jbio.201400017. Epub 2014 Aug 13.
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Vibrational biospectroscopy coupled with multivariate analysis extracts potentially diagnostic features in blood plasma/serum of ovarian cancer patients.振动生物光谱学与多元分析相结合,从卵巢癌患者的血浆/血清中提取潜在的诊断特征。
J Biophotonics. 2014 Apr;7(3-4):200-9. doi: 10.1002/jbio.201300157. Epub 2013 Nov 20.
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Epidemiology of dengue: past, present and future prospects.登革热的流行病学:过去、现在和未来展望。
Clin Epidemiol. 2013 Aug 20;5:299-309. doi: 10.2147/CLEP.S34440. eCollection 2013.
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Raman spectroscopy coupled with advanced statistics for differentiating menstrual and peripheral blood.拉曼光谱结合先进统计学方法区分月经血和外周血。
J Biophotonics. 2014 Jan;7(1-2):59-67. doi: 10.1002/jbio.201200191. Epub 2012 Nov 23.
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Protein subcellular localization of fluorescence imagery using spatial and transform domain features.使用空间和变换域特征对荧光图像进行蛋白质亚细胞定位。
Bioinformatics. 2012 Jan 1;28(1):91-7. doi: 10.1093/bioinformatics/btr624. Epub 2011 Nov 15.
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Evaluation of diagnostic tests: dengue.诊断检测评估:登革热
Nat Rev Microbiol. 2010 Dec;8(12 Suppl):S30-8. doi: 10.1038/nrmicro2459.
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Dengue fever: diagnosis and treatment.登革热:诊断与治疗。
Expert Rev Anti Infect Ther. 2010 Jul;8(7):841-5. doi: 10.1586/eri.10.53.
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Investigation of support vector machines and Raman spectroscopy for lymph node diagnostics.支持向量机和拉曼光谱在淋巴结诊断中的应用研究。
Analyst. 2010 May;135(5):895-901. doi: 10.1039/b920229c. Epub 2010 Mar 5.

基于拉曼光谱和支持向量机(SVM)的登革热感染分析。

Analysis of dengue infection based on Raman spectroscopy and support vector machine (SVM).

作者信息

Khan Saranjam, Ullah Rahat, Khan Asifullah, Wahab Noorul, Bilal Muhammad, Ahmed Mushtaq

机构信息

Agri-Biophotonics Division, National Institute for Lasers and Optronics (NILOP), Nilore, Islamabad 45650, Pakistan.

Pattern Recognition Lab, DCIS, Pakistan Institutes of Engineering and Applied Sciences (PIEAS), Nilore, Islamabad 45650, Pakistan.

出版信息

Biomed Opt Express. 2016 May 18;7(6):2249-56. doi: 10.1364/BOE.7.002249. eCollection 2016 Jun 1.

DOI:10.1364/BOE.7.002249
PMID:27375941
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC4918579/
Abstract

The current study presents the use of Raman spectroscopy combined with support vector machine (SVM) for the classification of dengue suspected human blood sera. Raman spectra for 84 clinically dengue suspected patients acquired from Holy Family Hospital, Rawalpindi, Pakistan, have been used in this study.The spectral differences between dengue positive and normal sera have been exploited by using effective machine learning techniques. In this regard, SVM models built on the basis of three different kernel functions including Gaussian radial basis function (RBF), polynomial function and linear functionhave been employed to classify the human blood sera based on features obtained from Raman Spectra.The classification model have been evaluated with the 10-fold cross validation method. In the present study, the best performance has been achieved for the polynomial kernel of order 1. A diagnostic accuracy of about 85% with the precision of 90%, sensitivity of 73% and specificity of 93% has been achieved under these conditions.

摘要

本研究介绍了拉曼光谱结合支持向量机(SVM)用于登革热疑似人类血清分类的情况。本研究使用了从巴基斯坦拉瓦尔品第圣家族医院采集的84例临床登革热疑似患者的拉曼光谱。通过有效的机器学习技术利用了登革热阳性血清和正常血清之间的光谱差异。在这方面,基于包括高斯径向基函数(RBF)、多项式函数和线性函数在内的三种不同核函数构建的支持向量机模型已被用于根据从拉曼光谱获得的特征对人类血清进行分类。分类模型已采用10折交叉验证法进行评估。在本研究中,一阶多项式核取得了最佳性能。在这些条件下,实现了约85%的诊断准确率、90%的精确率、73%的灵敏度和93%的特异性。