Bedoukian   RussellIPM   RussellIPM   Piezoelectric Micro-Sprayer


Home
Animal Taxa
Plant Taxa
Semiochemicals
Floral Compounds
Semiochemical Detail
Semiochemicals & Taxa
Synthesis
Control
Invasive spp.
References

Abstract

Guide

Alphascents
Pherobio
InsectScience
E-Econex
Counterpart-Semiochemicals
Print
Email to a Friend
Kindly Donate for The Pherobase

« Previous AbstractThe alpha-mating type locus of Cryptococcus neoformans contains a peptide pheromone gene    Next AbstractClassifying algorithms for SIFT-MS technology and medical diagnosis »

Annu Int Conf IEEE Eng Med Biol Soc


Title:Classification algorithms for SIFT-MS medical diagnosis
Author(s):Moorhead K; Lee D; Chase JG; Moot A; Ledingham K; Scotter J; Allardyce R; Senthilmohan S; Endre Z;
Address:"Department of Mechanical Engineering, University of Canterbury, Christchurch, New Zealand. ktm19@student.canterbury.ac.nz"
Journal Title:Annu Int Conf IEEE Eng Med Biol Soc
Year:2007
Volume:2007
Issue:
Page Number:5178 - 5181
DOI: 10.1109/IEMBS.2007.4353508
ISSN/ISBN:2375-7477 (Print) 2375-7477 (Linking)
Abstract:"Selected Ion Flow Tube-Mass Spectrometry (SIFT-MS) is an analytical technique for the real-time quantification of trace gases in air or breath samples. The SIFT-MS system can potentially offer unique capability in the early and rapid detection of a wide variety of diseases, infectious bacteria and patient conditions, by using a classifier to differentiate between control and test groups. By identifying which masses and Volatile Organic Compounds (VOCs) contribute most strongly towards a successful classification, biomarkers for a particular disease state may be discovered. A classification method is presented and validated in a simple study in which saturated nitrogen in tedlar bags was differentiated from dry nitrogen in tedlar bags. Several biomarkers were identified, with the most reliable being N2H(+).H2O, and isotopes and water clusters of H3O(+), as expected. The classifier was then applied in a clinical setting to differentiate between patient breath samples after one and four hours of dialysis treatment. Biomarkers for classification were ammonia, acetaldehyde, ethanol, isoprene and acetone. The model classifies significantly better than random, with an ROC area of 0.89"
Keywords:"*Algorithms Artificial Intelligence Breath Tests/*methods Data Interpretation, Statistical Diagnosis, Computer-Assisted/*methods Gases/*analysis Organic Chemicals/*analysis Pattern Recognition, Automated/*methods Reproducibility of Results Sensitivity and;"
Notes:"MedlineMoorhead, K Lee, D Chase, J G Moot, A Ledingham, K Scotter, J Allardyce, R Senthilmohan, S Endre, Z eng Evaluation Study Research Support, Non-U.S. Gov't 2007/11/16 Annu Int Conf IEEE Eng Med Biol Soc. 2007; 2007:5178-81. doi: 10.1109/IEMBS.2007.4353508"

 
Back to top
 
Citation: El-Sayed AM 2024. The Pherobase: Database of Pheromones and Semiochemicals. <http://www.pherobase.com>.
© 2003-2024 The Pherobase - Extensive Database of Pheromones and Semiochemicals. Ashraf M. El-Sayed.
Page created on 27-12-2024