Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Goal: Thermography-based infection screening at international airports plays an important role in the prevention of pandemics. Hover, studies show that thermography suffers from low sensitivity and specificity. To achieve higher screening accuracy,  developed a screening system based on the acquisition of multiple vital-signs. This multimodal approach increases accuracy, but introduces the need for sophisticated classification methods.  presents a comprehensive analysis of the multimodal approach to infection screening from a machine learning perspective.   Methods:   conduct an empirical study applying six classification algorithms to measurements from the multimodal screening system and comparing their performance among each other, as ll as to the performance of thermography. In addition,  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 been analyzed in our previous study using linear discriminant analysis. A total of 92 subjects re recruited for influenza screening using the system, consisting of 57 inpatients diagnosed to have seasonal influenza and 35 healthy controls.  Results:   Our study revealed that the multimodal screening system reduces the misclassification rate by more than 50% compared to thermography. At the same time, none of the multimodal classifiers needed more than 6 ms for classification, which is negligible for practical purposes.  Conclusion:  Among the tested classifiers k-nearest neighbors, support vector machine and quadratic discriminant analysis achieved the highest cross-validated sensitivity score of 93%.  Significance:  Multimodal infection screening might be able to address the shortcomings of thermography.
['Yu Yao', 'Guanghao Sun', 'Takemi Matsui', 'Yukiya Hakozaki', 'Stefan van Waasen', 'Michael Schiek']
I am writing a research paper on a idea Multiple Vital-Sign-Based Infection Screening Outperforms Thermography Independent of the Classification Algorithm
[{"from": "human", "value": "I am writing a research paper on a idea Multiple Vital-Sign-Based Infection Screening Outperforms Thermography Independent of the Classification Algorithm"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Goal: Thermography-based infection screening at international airports plays an important role in the prevention of pandemics. Hover, studies show that thermography suffers from low sensitivity and specificity. To achieve higher screening accuracy,  developed a screening system based on the acquisition of multiple vital-signs. This multimodal approach increases accuracy, but introduces the need for sophisticated classification methods.  presents a comprehensive analysis of the multimodal approach to infection screening from a machine learning perspective.   Methods:   conduct an empirical study applying six classification algorithms to measurements from the multimodal screening system and comparing their performance among each other, as ll as to the performance of thermography. In addition,  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 been analyzed in our previous study using linear discriminant analysis. A total of 92 subjects re recruited for influenza screening using the system, consisting of 57 inpatients diagnosed to have seasonal influenza and 35 healthy controls.  Results:   Our study revealed that the multimodal screening system reduces the misclassification rate by more than 50% compared to thermography. At the same time, none of the multimodal classifiers needed more than 6 ms for classification, which is negligible for practical purposes.  Conclusion:  Among the tested classifiers k-nearest neighbors, support vector machine and quadratic discriminant analysis achieved the highest cross-validated sensitivity score of 93%.  Significance:  Multimodal infection screening might be able to address the shortcomings of thermography."}]