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| 001 | 256018 | ||
| 005 | 20250129092417.0 | ||
| 024 | 7 | _ | |a 10.1109/TBME.2015.2479716 |2 doi |
| 024 | 7 | _ | |a 0018-9294 |2 ISSN |
| 024 | 7 | _ | |a 1558-2531 |2 ISSN |
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| 037 | _ | _ | |a FZJ-2015-06050 |
| 082 | _ | _ | |a 610 |
| 100 | 1 | _ | |a Yao, Yu |0 P:(DE-Juel1)144822 |b 0 |e Corresponding author |
| 245 | _ | _ | |a Multiple Vital-sign Based Infection Screening Outperforms Thermography Independent of the Classification Algorithm |
| 260 | _ | _ | |a New York, NY |c 2015 |b IEEE |
| 336 | 7 | _ | |a Journal Article |b journal |m journal |0 PUB:(DE-HGF)16 |s 1452496360_899 |2 PUB:(DE-HGF) |
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| 520 | _ | _ | |a 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. |
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