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Opis

Demystifying Big Data, Machine Learning, and Deep Learning for Healthcare Analytics presents the changing the world of data utilization, especially in clinical healthcare. Various techniques, methodologies, algorithms are presented in this book by researchers to organize data in a structured manner that will assist physicians in the precision care of patients and help biomedical engineeris and computers scientists understand the impact of these techniques on healthcare analytics. The book is divided into two Parts. Part 1 covers the Big Data aspects, i.e., healthcare Decision Support Systems and Analytics related topics. Part 2 focuses on the current frameworks and applications of Deep Learning and Machine Learning, and provides an outlook on future directions of research and development as well. The entire book takes a Case Study Approach, providing a wealth of real-world case studies in the application chapters to act as a foundational reference for biomedical engineers, computer scientists, and healthcare researchers and clinicians. Provides a comprehensive reference for biomedical engineers, computer sciences, advanced industry practitioners, researchers, and clinicians to understand and develop healthcare analytics using advanced tools and technologiesIncludes in-depth illustration of advanced techniques via dataset samples, statistical tables and graphs with algorithms and computational methods for developing new applications in healthcare informaticsUnique case study approach provides readers with insights for practical clinical implementations

Szczegóły książki

Kategoria Książki po angielsku Medicine Nursing & ancillary services Biomedical engineering

• Pełny tytuł: Demystifying Big Data, Machine Learning, and Deep Learning for Healthcare Analytics
• Autor: Pradeep Nijalingappa, Sandeep Kautish, Sheng Peng
• Język: Angielski
• Oprawa: Miękka
• Liczba stron: 372
• EAN: 9780128216330
• ID: 33382408
• Wydawca: Elsevier Science Publishing Co Inc
• Waga: 764 g
• Wymiary: 191 × 232 × 27 mm
• Data wydania: 14. June 2021

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