Artificial Intelligence for Drug Development, Precision Medicine, and Healthcare (eBook, ePUB)
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Artificial Intelligence for Drug Development, Precision Medicine, and Healthcare covers exciting developments at the intersection of computer science and statistics. While much of machine-learning is statistics-based, achievements in deep learning for image and language processing rely on computer science's use of big data. Aimed at those with a statistical background who want to use their strengths in pursuing AI research, the book:· Covers broad AI topics in drug development, precision medicine, and healthcare.· Elaborates on supervised, unsupervised, reinforcement, and evolutionary learni...
Artificial Intelligence for Drug Development, Precision Medicine, and Healthcare covers exciting developments at the intersection of computer science and statistics. While much of machine-learning is statistics-based, achievements in deep learning for image and language processing rely on computer science's use of big data. Aimed at those with a statistical background who want to use their strengths in pursuing AI research, the book:
· Covers broad AI topics in drug development, precision medicine, and healthcare.
· Elaborates on supervised, unsupervised, reinforcement, and evolutionary learning methods.
· Introduces the similarity principle and related AI methods for both big and small data problems.
· Offers a balance of statistical and algorithm-based approaches to AI.
· Provides examples and real-world applications with hands-on R code.
· Suggests the path forward for AI in medicine and artificial general intelligence.
As well as covering the history of AI and the innovative ideas, methodologies and software implementation of the field, the book offers a comprehensive review of AI applications in medical sciences. In addition, readers will benefit from hands on exercises, with included R code.
· Covers broad AI topics in drug development, precision medicine, and healthcare.
· Elaborates on supervised, unsupervised, reinforcement, and evolutionary learning methods.
· Introduces the similarity principle and related AI methods for both big and small data problems.
· Offers a balance of statistical and algorithm-based approaches to AI.
· Provides examples and real-world applications with hands-on R code.
· Suggests the path forward for AI in medicine and artificial general intelligence.
As well as covering the history of AI and the innovative ideas, methodologies and software implementation of the field, the book offers a comprehensive review of AI applications in medical sciences. In addition, readers will benefit from hands on exercises, with included R code.
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