Applications of Machine Learning in Digital Healthcare

ISBN 10: 1839533358 / ISBN 13: 9781839533358

Edité par Institution of Engineering and Technology, GB, 2023

A propos de cet article

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.

N° de réf. du vendeur LU-9781839533358

Détails bibliographiques

• Titre: Applications of Machine Learning in Digital Healthcare
• Éditeur: Institution of Engineering and Technology, GB
• Date de parution: 2023
• Langue: anglais
• ISBN à 10 chiffres: 1839533358
• ISBN à 13 chiffres: 9781839533358
• Reliure: Hardback
• État de l'article: New

A propos de ce titre

Synopsis

Machine learning is a type of artificial intelligence in which computer algorithms are built to learn to perform set tasks, then use the data acquired to improve the performance of the processing of that data.

At the macro-level, ML can be applied to look at health records of different segments of the population (eg. elderly, pregnant, asthmatic, stroke etc.) to assist with evidence-based policy decisions. At a lower scale, ML techniques can be applied to data acquired from an individual patient's brain, heart, muscles, medical images etc. and cross referenced against "healthy" data to aid diagnosis.

Large amounts of information are often necessary for diagnosis and treatment of a condition. The volume and complexity of the data makes the physician's job difficult and provides a compelling reason for the development of machine learning based tools to aid them.

À propos des auteurs

Miguel Hernandez Silveira is the CEO and a principal consultant at Medical Frontier Technology Ltd, UK. He is also CTO of SENTI TECH LTD, UK. He held positions as visiting lecturer at the University of Surrey, UK, and a visiting researcher at Imperial College London, UK. He is also a member of the IET Healthcare Technical Profession Network Committee, and reviewer of IEEE Sensors and IEEE Biomedical Circuits and Systems Journals. His research interests include machine learning, wireless low-power healthcare systems, biomedical sensors, instruments and algorithms, and digital signal processing.

Su-Shin Ang is the CEO and a principal consultant at Medical Frontier Technology Asia Pte Ltd, Singapore. He is a practising engineer, whose passion lies in the application of cutting-edge technology to the improvement of patient care. His research interests include machine learning, healthcare technology, development and deployment of medical devices, and the Internet of medical things.