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@ARTICLE{Yao:256018,
      author       = {Yao, Yu and Sun, Guanghao and Matsui, Takemi and Hakozaki,
                      Yukiya and van Waasen, Stefan and Schiek, Michael},
      title        = {{M}ultiple {V}ital-sign {B}ased {I}nfection {S}creening
                      {O}utperforms {T}hermography {I}ndependent of the
                      {C}lassification {A}lgorithm},
      journal      = {IEEE transactions on biomedical engineering},
      volume       = {vv},
      number       = {99},
      issn         = {1558-2531},
      address      = {New York, NY},
      publisher    = {IEEE},
      reportid     = {FZJ-2015-06050},
      pages        = {1 - 1},
      year         = {2015},
      abstract     = {Goal: Thermography based infection screening at
                      international airports plays an important role in the
                      prevention of pandemics. However, studies show that
                      thermography suffers from low sensitivity and specificity.
                      To achieve higher screeningaccuracy, we developed a
                      screening system based on the acquisition of multiple
                      vital-signs. This multi-modal approach increases accuracy,
                      but introduces the need for sophisticated classification
                      methods. This paper presents a comprehensive analysis of the
                      multi-modal approach to infection screening from a machine
                      learning perspective. Methods: We conduct an empirical study
                      applying six classification algorithms to measurements from
                      the multi-modal screening system and comparing their
                      performance among each other, as well as to the performance
                      of thermography. In addition, we provide an information
                      theoretic view on the use of multiple vital-signs for
                      infection screening. The classification methods are tested
                      using the same clinical data which has beenanalysed in our
                      previous study using linear discriminant analysis. A total
                      of 92 subjects were recruited for influenza screening using
                      the system, consisting of 57 inpatients diagnosed to have
                      seasonal influenza and 35 healthy controls. Results: Our
                      study revealedthat the multi-modal screening system reduces
                      the misclassification rate by more than $50\%$ compared to
                      thermography. At the same time, none of the multi-modal
                      classifiers needed more than 6 ms for classification, which
                      is negligible for practical purposes. Conclusion: Among the
                      tested classifiers k-nearest neighbours, support vector
                      machine and quadratic discriminant analysis achieved the
                      highest cross-validated sensitivity score of $93\%.$
                      Significance: Multi-modal infection screening might be able
                      to address the shortcomings of thermography.},
      cin          = {ZEA-2},
      ddc          = {610},
      cid          = {I:(DE-Juel1)ZEA-2-20090406},
      pnm          = {574 - Theory, modelling and simulation (POF3-574)},
      pid          = {G:(DE-HGF)POF3-574},
      typ          = {PUB:(DE-HGF)16},
      UT           = {WOS:000375001600015},
      doi          = {10.1109/TBME.2015.2479716},
      url          = {https://juser.fz-juelich.de/record/256018},
}