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Deep Learning. --- Precision Medicine. --- Patient-Specific Modeling. --- Decision Support Systems, Clinical. --- Clinical Decision Support --- Decision Support, Clinical --- Clinical Decision Support System --- Clinical Decision Support Systems --- Clinical Decision Supports --- Decision Supports, Clinical --- Support, Clinical Decision --- Supports, Clinical Decision --- Clinical Decision-Making --- Patient-Specific Computational Modeling --- Physiome --- Computational Modeling, Patient-Specific --- Modeling, Patient-Specific --- Patient Specific Computational Modeling --- Patient Specific Modeling --- Physiomes --- Models, Biological --- Precision Medicine --- P Health --- P-Health --- Personalized Medicine --- Theranostics --- Individualized Medicine --- Predictive Medicine --- Medicine, Individualized --- Medicine, Personalized --- Medicine, Precision --- Medicine, Predictive --- Theranostic --- Pharmacogenomic Variants --- Pharmacogenetics --- Patient-Specific Modeling --- Hierarchical Learning --- Learning, Deep --- Learning, Hierarchical --- Precision medicine --- Deep learning (Machine learning) --- Artificial intelligence --- Data processing. --- Medical applications.
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This book comprehensively reviews the potential of Artificial Intelligence (AI) in biomedical research and healthcare, with a major emphasis on virology. The initial chapter presents the applications of machine learning methods for structured data, such as the classical support vector machine and neural network, modern deep learning, and natural language processing for unstructured data in biomedical research and healthcare. The subsequent chapters explore the applications of AI in tackling COVID-19, analysis of the pandemic, viral infection, disease spread, and control. The book further identifies the potential applications of machine learning in the field of virology with a focus on the key aspects of infection: diagnosis, transmission, response to treatment, and resistance. The book also discusses progress and challenges in developing viral vaccines and examines the application of viruses in translational research and human healthcare. Furthermore, the book covers the applications of artificial intelligence-mediated diagnosis and the development of drugs to treat the disease. Towards the end, the book summarizes the ethical and legal challenges posed by AI in healthcare and biomedical research. This book is an invaluable source for researchers, medical and industry practitioners, academicians, and students exploring the applications of AI in biomedical research and healthcare.
Virology. --- Diseases—Causes and theories of causation. --- Artificial intelligence. --- Pathogenesis. --- Artificial Intelligence. --- AI (Artificial intelligence) --- Artificial thinking --- Electronic brains --- Intellectronics --- Intelligence, Artificial --- Intelligent machines --- Machine intelligence --- Thinking, Artificial --- Bionics --- Cognitive science --- Digital computer simulation --- Electronic data processing --- Logic machines --- Machine theory --- Self-organizing systems --- Simulation methods --- Fifth generation computers --- Neural computers --- Microbiology --- Artificial intelligence --- Medical applications. --- Medicine --- Data processing
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This book comprehensively reviews the potential of Artificial Intelligence (AI) in biomedical research and healthcare, with a major emphasis on virology. The initial chapter presents the applications of machine learning methods for structured data, such as the classical support vector machine and neural network, modern deep learning, and natural language processing for unstructured data in biomedical research and healthcare. The subsequent chapters explore the applications of AI in tackling COVID-19, analysis of the pandemic, viral infection, disease spread, and control. The book further identifies the potential applications of machine learning in the field of virology with a focus on the key aspects of infection: diagnosis, transmission, response to treatment, and resistance. The book also discusses progress and challenges in developing viral vaccines and examines the application of viruses in translational research and human healthcare. Furthermore, the book covers the applications of artificial intelligence-mediated diagnosis and the development of drugs to treat the disease. Towards the end, the book summarizes the ethical and legal challenges posed by AI in healthcare and biomedical research. This book is an invaluable source for researchers, medical and industry practitioners, academicians, and students exploring the applications of AI in biomedical research and healthcare.
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