Mark Chang

Artificial Intelligence for Drug Development, Precision Medicine, and Healthcare (eBook, PDF)

• Format: PDF

Produktbeschreibung

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.

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Produktdetails

• Verlag: Taylor & Francis eBooks
• Seitenzahl: 368
• Erscheinungstermin: 7. Mai 2020
• Englisch
• ISBN-13: 9781000766721
• Artikelnr.: 59399467

Autorenporträt

Mark Chang is the founder of AGInception. With 12 published books, he is an adjunct professor at Boston University, an elected Fellow of the American Statistical Association, and a cofounder of the International Society for Biopharmaceutical Statistics.

Inhaltsangabe

1. Overview of Modern Artificial Intelligence. 2. Classic Statistics and
Modern Machine Learning. 3. Similarity Principle- Fundamental Principle of
All Sciences. 4. Similarity-Principle-Based Artificial Intelligence. 5.
Artificial Neural Network. 6. Deep Learning Neural Network. 7. Kernel
Methods. 8. Decision Tree and Ensemble Methods. 9. Bayesian Learning
Approach. 10. Unsupervised Learning. 11. Reinforcement Learning. 12. Swarm
and Evolutionary Intelligence. 13. Applications of AI in Medical Science
and Drug Development. 14. Future Perspectives-Artificial General
Intelligence.