Molecular Pathogens 2024, Vol.15, No.1, 1-8 http://microbescipublisher.com/index.php/mp 4 Artificial intelligence can also be used for drug development and optimization of treatment plans for influenza viruses (Figure 2). By utilizing machine learning and data mining techniques, large-scale drug databases and biological information can be analyzed to identify potential antiviral drugs and therapeutic targets. Artificial intelligence can assist researchers in virtual screening and drug design, accelerating the speed and efficiency of drug discovery. Additionally, by analyzing individual patient characteristics and disease conditions, artificial intelligence can develop personalized treatment plans to improve treatment efficacy and prevention. Figure 2 Using artificial intelligence to diagnose influenza A (H1N1) virus infection (Picture source: Sohu) 3.2 The application of data-driven predictive models in the diagnosis of influenza A (H1N1) virus infection Data-driven predictive models have vast applications in the diagnosis of influenza A (H1N1) virus infection. By analyzing large-scale case data and patient information, these models can assist doctors in making more accurate and efficient diagnoses. Data-driven predictive models utilize machine learning and deep learning algorithms to extract valuable information from multidimensional data, including clinical manifestations, laboratory test results, epidemiological characteristics, and more, to further predict the likelihood of a patient being infected with influenza A (H1N1) virus. The learning and training processes of these models are based on historical case data, continuously optimizing the model parameters to improve prediction accuracy (Winter and Carusi, 2022). Data-driven predictive models can incorporate various external factors such as geographic location, climate change, and socio-economic factors to predict the transmission trends of influenza A (H1N1) virus. These factors can potentially affect the speed and scope of virus transmission, consequently impacting the spread of the epidemic. By analyzing these factors, the models can assist doctors and public health agencies in implementing preventive measures in advance, slowing down the rate of epidemic spread. Data-driven predictive models can also assess the effectiveness of antiviral drugs. By analyzing clinical trial data and real-world data, the models can predict the treatment outcomes of antiviral drugs for different populations, providing doctors with more reference guidance. Meanwhile, these models can personalize the best treatment plan for each patient based on individual variations. As technology advances and data quality improves, the application of these models will become increasingly widespread, offering more assistance and support to prevention, control, and treatment efforts. 3.3 Methods and achievements of using deep learning for the diagnosis of influenza A (H1N1) virus infection Deep learning models can be utilized to predict whether patients are infected with influenza A (H1N1) virus. These predictive models are based on a large volume of case data and learn the features of these cases to diagnose new incoming cases. Deep learning models exhibit high sensitivity and specificity, effectively improving diagnostic accuracy, and can process large amounts of case data in a short amount of time.
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