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Applications of Machine Learning in Digital Healthcare (Healthcare Technologies)

Applications of Machine Learning in Digital Healthcare (Healthcare Technologies)

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ISBN

978-1839533358

Authors

Publisher

Other Publisher

Published Date

2023

Language

‎English

Page Count

1326

Size

17 MB

By Miguel Hernandez Silveira, Su-Shin Ang Machine learning algorithms are increasingly finding applications in the healthcare sector. Whether assisting a clinician to process an individual patient's data or helping administrators view hospital bed turnover, the volume and complexity of healthcare data is a compelling reason for the development of machine learning based tools to aid in its interpretation and use. This edited book focuses on the applications of machine learning in the healthcare sector, both at the macro-level for guiding policy decisions, and at the granular level, showing how ML techniques can be applied to process an individual patient's medical data to swiftly aid diagnosis. Written by an international team of experts, the book presents several applications of machine learning in the healthcare sector, including health system planning, optimisation and preparedness, outlining the benefits and challenges of coordination and data sharing. Machine learning has many applications in processing patient data and topics such as arrhythmia detection, image-guided microsurgery and early detection of Alzheimer's disease are discussed in depth. The book also looks at machine learning applications exploiting wearable sensors for real-time analysis and concepts around enhancing physical performance. Suitable for an audience of computer scientists, healthcare engineers and those involved with digital medicine, this book brings together a plethora of machine learning applications from across the board of the healthcare services. Product Details Publisher ‏ : ‎ The Institution of Engineering and Technology (June 6, 2023) Language ‏ : ‎ English Hardcover ‏ : ‎ 416 pages ISBN-10 ‏ : ‎ 1839533358 ISBN-13 ‏ : ‎ 978-1839533358

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