Abstract
Norovirus monitoring and early warning can be used for diagnosis without etiological testing, and the treatment of this disease does not require the antibiotics. It often occurs in preschool children and affects their growth and development, so the coping measures for this disease are more prevention than treatment. In this study, the clinical data of 2133 children with diarrhea were collected. Based on the artificial intelligence (AI) algorithm of wavelet transform, a related model for data mining and processing of children's intestinal ultrasound images and stool specimens was constructed. Then, the norovirus infection trend was warned based on the wavelet analysis algorithm model. The results showed that the intestinal ultrasound image processed by the wavelet transform algorithm was clearer. The positive detection rate of norovirus in children with clinical diarrhea was as high as 59%, and the children had different degrees of body damage, of which the probability of compensatory metabolic acidosis was the highest. The epidemiological analysis found that children with norovirus infection were mainly concentrated in the age group under 2 years old and over 5 years old and showed a peak of infection in December. In summary, the intelligent algorithm based on wavelet transform can realize the noise reduction of intestinal ultrasound, and it should protect children with susceptible age and susceptible seasons to reduce the clinical infection rate of norovirus.
Highlights
Norovirus, known as Norwalk virus, is highly similar to common viruses except for antigenicity [1]. e diarrhea caused by the virus is extremely prevalent, and infection can occur throughout the year. e main population of the disease includes adults and school-age children, with a high incidence in cold seasons [2]
Compared with the Contourlet threshold and Donoho threshold, the wavelet transform algorithm used in this study showed a higher signal-to-noise ratio (SNR) value after ultrasonic image denoising and a smaller mean square error (MSE) value
The main focus was the application of monitoring and early warning detection technology of norovirus, namely, data mining technology. erefore, a large number of children patients with diarrhea symptoms were selected, and the ultrasonic examination results were collected
Summary
Known as Norwalk virus, is highly similar to common viruses except for antigenicity [1]. e diarrhea caused by the virus is extremely prevalent, and infection can occur throughout the year. e main population of the disease includes adults and school-age children, with a high incidence in cold seasons [2]. Known as Norwalk virus, is highly similar to common viruses except for antigenicity [1]. E diarrhea caused by the virus is extremely prevalent, and infection can occur throughout the year. In Western developed countries, more than 80% of all nonbacterial diarrhea outbreaks are infected by the virus every year. Among children patients with diarrhea under 6 years old in China, the detection rate of norovirus is about 17%, and it has been found that the infection of this virus is extremely common in the population of China through blood routine investigation [4]. Since the winter of 2014, norovirus outbreaks have increased significantly, higher markedly than previous years, so monitoring and early warning of this virus has become important [6]
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