Demystifying Big Data, Machine Learning, and Deep Learning for Healthcare Analytics /
Demystifying Big Data, Machine Learning, and Deep Learning for Healthcare Analytics presents the changing world of data utilization, especially in clinical healthcare. Various techniques, methodologies, and algorithms are presented in this book to organize data in a structured manner that will assis...
| Glavni autori: | , , , |
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| Format: | software, multimedia |
| Jezik: | eng |
| Izdano: |
London : Academic Press,
2021
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| Teme: | |
| Online pristup: | https://www.sciencedirect.com/science/book/9780128216330 |
| _version_ | 1826472419476373504 |
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| author | Nijalingappa, Pradeep, editor 651015 Kautish, Sandeep, editor 651016 Peng, Sheng-Lung, editor 636855 ScienceDirect (Online service) 7722 |
| author_facet | Nijalingappa, Pradeep, editor 651015 Kautish, Sandeep, editor 651016 Peng, Sheng-Lung, editor 636855 ScienceDirect (Online service) 7722 |
| author_sort | Nijalingappa, Pradeep, editor 651015 |
| collection | OCEAN |
| description | Demystifying Big Data, Machine Learning, and Deep Learning for Healthcare Analytics presents the changing world of data utilization, especially in clinical healthcare. Various techniques, methodologies, and algorithms are presented in this book to organize data in a structured manner that will assist physicians in the care of patients and help biomedical engineers and computer scientists understand the impact of these techniques on healthcare analytics. The book is divided into two parts: Part 1 covers big data aspects such as 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. 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, healthcare researchers, and clinicians. |
| first_indexed | 2024-03-05T17:18:13Z |
| format | software, multimedia |
| id | KOHA-OAI-TEST:605773 |
| institution | Universiti Teknologi Malaysia - OCEAN |
| language | eng |
| last_indexed | 2024-03-05T17:18:13Z |
| publishDate | 2021 |
| publisher | London : Academic Press, |
| record_format | dspace |
| spelling | KOHA-OAI-TEST:6057732023-09-27T08:17:20ZDemystifying Big Data, Machine Learning, and Deep Learning for Healthcare Analytics / Nijalingappa, Pradeep, editor 651015 Kautish, Sandeep, editor 651016 Peng, Sheng-Lung, editor 636855 ScienceDirect (Online service) 7722 software, multimedia Electronic books 631902 London : Academic Press,©20212021engDemystifying Big Data, Machine Learning, and Deep Learning for Healthcare Analytics presents the changing world of data utilization, especially in clinical healthcare. Various techniques, methodologies, and algorithms are presented in this book to organize data in a structured manner that will assist physicians in the care of patients and help biomedical engineers and computer scientists understand the impact of these techniques on healthcare analytics. The book is divided into two parts: Part 1 covers big data aspects such as 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. 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, healthcare researchers, and clinicians.Includes bibliographical references and indexSection 1. Big Data in Healthcare Analytics -- Section 2. Machine Learning and Deep Learning for Healthcare.Demystifying Big Data, Machine Learning, and Deep Learning for Healthcare Analytics presents the changing world of data utilization, especially in clinical healthcare. Various techniques, methodologies, and algorithms are presented in this book to organize data in a structured manner that will assist physicians in the care of patients and help biomedical engineers and computer scientists understand the impact of these techniques on healthcare analytics. The book is divided into two parts: Part 1 covers big data aspects such as 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. 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, healthcare researchers, and clinicians.Medical informaticsBig dataMachine learninghttps://www.sciencedirect.com/science/book/9780128216330URN:ISBN:9780128216330Remote access restricted to users with a valid UTM ID via VPN |
| spellingShingle | Medical informatics Big data Machine learning Nijalingappa, Pradeep, editor 651015 Kautish, Sandeep, editor 651016 Peng, Sheng-Lung, editor 636855 ScienceDirect (Online service) 7722 Demystifying Big Data, Machine Learning, and Deep Learning for Healthcare Analytics / |
| title | Demystifying Big Data, Machine Learning, and Deep Learning for Healthcare Analytics / |
| title_full | Demystifying Big Data, Machine Learning, and Deep Learning for Healthcare Analytics / |
| title_fullStr | Demystifying Big Data, Machine Learning, and Deep Learning for Healthcare Analytics / |
| title_full_unstemmed | Demystifying Big Data, Machine Learning, and Deep Learning for Healthcare Analytics / |
| title_short | Demystifying Big Data, Machine Learning, and Deep Learning for Healthcare Analytics / |
| title_sort | demystifying big data machine learning and deep learning for healthcare analytics |
| topic | Medical informatics Big data Machine learning |
| url | https://www.sciencedirect.com/science/book/9780128216330 |
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