Explore the theory and practical applications of artificial intelligence (AI) and machine learning in healthcare. This book offers a guided tour of machine learning algorithms, architecture design, and applications of learning in healthcare and big data challenges.

You’ll discover the ethical implications of healthcare data analytics and the future of AI in population and patient health optimization.   You’ll also create a machine learning model, evaluate performance and operationalize its outcomes within your organization.

Machine Learning and AI for Healthcare provides techniques on how to apply machine learning within your organization and evaluate the efficacy, suitability, and efficiency of AI applications. These are illustrated through leading case studies, including how chronic disease is being redefined through patient-led data learning and the Internet of Things.

• Gain a deeper understanding of key machine learning algorithms and their use and implementation within wider healthcare
• Implement machine learning systems, such as speech recognition and enhanced deep learning/AI
• Select learning methods/algorithms and tuning for use in healthcare
• Recognize and prepare for the future of artificial intelligence in healthcare through best practices, feedback loops and intelligent agents

• Discover convolutional nets and how to use them
• Build deep feedforward nets using locally connected layers, pooling layers, and softmax outputs
• Master the various programming algorithms required
• Carry out multi-threaded gradient computations and memory allocations for this threading
• Work with CUDA code implementations of all core computations, including layer activations and gradient calculations
• Make use of the CONVNET program and manual to explore convolutional nets and case studies