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.

This book is a practical introduction to the safe operation and control of critical systems in defense, industrial, and healthcare applications. It highlights system engineering processes and presents an overview of the equipment health monitoring (EHM) functional architecture and algorithm design. The book also explores machine learning functions, such as feature extraction, data visualization and model boundaries. The need for intelligent diagnostics and proposed health monitoring framework is increasingly important within sensing technology, big data analytics and grid capabilities. This resource, packed with case studies from industrial and healthcare settings, identifies key problems along with various techniques that address the current issues as well as future developments in the field. A MATLAB code is included to assist engineers with projects in the field.