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Influenza, Human

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Novel graph-based machine-learning technique for viral infectious diseases: application to influenza and hepatitis diseases.

Annals of medicine
BACKGROUND: Most infectious diseases are caused by viruses, fungi, bacteria and parasites. Their ability to easily infect humans and trigger large-scale epidemics makes them a public health concern. Methods for early detection of these diseases have ...

Influenza Epidemic Trend Surveillance and Prediction Based on Search Engine Data: Deep Learning Model Study.

Journal of medical Internet research
BACKGROUND: Influenza outbreaks pose a significant threat to global public health. Traditional surveillance systems and simple algorithms often struggle to predict influenza outbreaks in an accurate and timely manner. Big data and modern technology h...

Prediction of the number of asthma patients using environmental factors based on deep learning algorithms.

Respiratory research
BACKGROUND: Air pollution, weather, pollen, and influenza are typical aggravating factors for asthma. Previous studies have identified risk factors using regression-based and ensemble models. However, studies that consider complex relationships and i...

Integrated epigenomic exposure signature discovery.

Epigenomics
The epigenome influences gene regulation and phenotypes in response to exposures. Epigenome assessment can determine exposure history aiding in diagnosis. Here we developed and implemented a machine learning algorithm, the exposure signature discove...

Deep-learning assisted zwitterionic magnetic immunochromatographic assays for multiplex diagnosis of biomarkers.

Talanta
Magnetic nanoparticle (MNP)-based immunochromatographic tests (ICTs) display long-term stability and an enhanced capability for multiplex biomarker detection, surpassing conventional gold nanoparticles (AuNPs) and fluorescence-based ICTs. In this stu...

Seasonal antigenic prediction of influenza A H3N2 using machine learning.

Nature communications
Antigenic characterization of circulating influenza A virus (IAV) isolates is routinely assessed by using the hemagglutination inhibition (HI) assays for surveillance purposes. It is also used to determine the need for annual influenza vaccine update...

Prediction of hospital-acquired influenza using machine learning algorithms: a comparative study.

BMC infectious diseases
BACKGROUND: Hospital-acquired influenza (HAI) is under-recognized despite its high morbidity and poor health outcomes. The early detection of HAI is crucial for curbing its transmission in hospital settings.

Estimating epidemic trajectories of SARS-CoV-2 and influenza A virus based on wastewater monitoring and a novel machine learning algorithm.

The Science of the total environment
The COVID-19 pandemic has altered the circulation of non-SARS-CoV-2 respiratory viruses. In this study, we carried out wastewater surveillance of SARS-CoV-2 and influenza A virus (IAV) in three key port cities in China through real-time quantitative ...

Forecasting and analyzing influenza activity in Hebei Province, China, using a CNN-LSTM hybrid model.

BMC public health
BACKGROUND: Influenza, an acute infectious respiratory disease, presents a significant global health challenge. Accurate prediction of influenza activity is crucial for reducing its impact. Therefore, this study seeks to develop a hybrid Convolution ...

FluPMT: Prediction of Predominant Strains of Influenza A Viruses via Multi-Task Learning.

IEEE/ACM transactions on computational biology and bioinformatics
Seasonal influenza vaccines play a crucial role in saving numerous lives annually. However, the constant evolution of the influenza A virus necessitates frequent vaccine updates to ensure its ongoing effectiveness. The decision to develop a new vacci...