This book provides a snapshot of the state of current research at the interface between machine learning and healthcare with special emphasis on machine learning projects that are (or are close to) achieving improvement in patient outcomes. The book provides overviews on a range of technologies including detecting artefactual events in vital signs monitoring data; patient physiological monitoring; tracking infectious disease; predicting antibiotic resistance from genomic data; and managing chronic disease. With contributions from an international panel of leading researchers, this book will find a place on the bookshelves of academic and industrial researchers and advanced students working in healthcare technologies, biomedical engineering, and machine learning.
Topics covered include;
David Clifton is Associate Professor of Engineering Science at the University of Oxford, and a Research Fellow of the Royal Academy of Engineering. He leads the Computational Health Informatics Laboratory at the Institute of Biomedical Engineering in Oxford's Department of Engineering Science. Prof. Clifton’s research focuses on the development of "big data" machine learning for tracking the health of complex systems. He previously worked on the world's first FDA-approved multivariate patient monitoring system, and systems that are used to monitor 20,000 patients each month in the UK National Health Service.
This book is essential reading for Academic and Industrial researchers and advanced students working in healthcare technologies, biomedical engineering and machine learning. It is also recommended to commercial developers of computing-based healthcare applications.