Classifying algorithms for SIFT-MS technology and medical diagnosis

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dc.contributor.authorMoorhead, K.T.
dc.contributor.authorLee, D.S.
dc.contributor.authorChase, Geoff
dc.contributor.authorMoot, A.R.
dc.contributor.authorLedingham, K.
dc.contributor.authorScotter, J.
dc.contributor.authorAllardyce, R.
dc.contributor.authorSentilomohan, S.T.
dc.contributor.authorEndre, Z.
dc.date.accessioned2009-05-13T21:25:23Z
dc.date.available2009-05-13T21:25:23Z
dc.date.issued2008en
dc.description.abstractSelected Ion Flow Tube-Mass spectrometry (SIFT-MS) is an analytical technique for realtime quantification of trace gases in air or breath samples. SIFT-MS system thus offers unique potential for early, rapid detection of disease states. Identification of volatile organic compound (VOC) masses that contribute strongly towards a successful classification clearly highlights potential new biomarkers. A method utilising kernel density estimates is thus presented for classifying unknown samples. It is validated in a simple known case and a clinical setting before–after dialysis. The simple case with nitrogen in Tedlar bags returned a 100% success rate, as expected. The clinical proof-of-concept with seven tests on one patient had an ROC curve area of 0.89. These results validate the method presented and illustrate the emerging clinical potential of this technology.en
dc.identifier.citationMoorhead, K.T., Lee, D.S., Chase, J.G., Moot, A.R., Ledingham, K., Scotter, J., Allardyce, R., Sentilomohan, S.T., Endre, Z. (2008) Classifying algorithms for SIFT-MS technology and medical diagnosis. Computer Methods and Programs in Biomedicine, 89(3), pp. 226-238.en
dc.identifier.doihttps://doi.org/10.1016/j.cmpb.2007.11.011
dc.identifier.issn0169-2607
dc.identifier.urihttp://hdl.handle.net/10092/2443
dc.language.isoen
dc.publisherUniversity of Canterbury. Mathematics and Statisticsen
dc.publisherUniversity of Canterbury. Mechanical Engineeringen
dc.rights.urihttps://hdl.handle.net/10092/17651en
dc.subjectkernel classifieren
dc.subjectclassificationen
dc.subjectSIFT-MSen
dc.subjectdiagnosticsen
dc.subjectbreath analysisen
dc.subjectVOCen
dc.subject.marsdenFields of Research::280000 Information, Computing and Communication Sciences::280400 Computation Theory and Mathematicsen
dc.subject.marsdenFields of Research::250000 Chemical Sciences::250400 Analytical Chemistry::250402 Analytical spectrometryen
dc.titleClassifying algorithms for SIFT-MS technology and medical diagnosisen
dc.typeJournal Article
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