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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.

Medical images can highlight differences between healthy tissue and unhealthy tissue and these images can then be assessed by a healthcare professional to identify the stage and spread of a disease so a treatment path can be established. With machine learning techniques becoming more prevalent in healthcare, algorithms can be trained to identify healthy or unhealthy tissues and quickly differentiate between the two. Statistical models can be used to process numerous images of the same type in a fraction of the time it would take a human to assess the same quantity, saving time and money in aiding practitioners in their assessment.

The evolution of medical equipment engineering is progressing rapidly, with advances in digital healthcare technologies such as artificial intelligence, virtual/augmented reality, 3D-printing, robotics and nanotechnologies developing at pace. Medical equipment engineering can assist in surveying workplace inefficiencies, and develop efficient optimisation processes, through data research and intelligent learning automation. This edited book covers the benefits of the integration of lean manufacturing, smart sensors, 5G technology, IoTs, virtual reality, 3D printing, robotics and automation.

This edited book explores the use of mobile technologies such as phones, drones, robots, apps, and wearable monitoring devices for improving access to healthcare for socially disadvantaged populations in remote, rural or developing regions. This book brings together examples of large scale, international projects from developing regions of China and Belt and Road countries from researchers in Australia, Bangladesh, Denmark, Norway, Japan, Spain, Thailand and China. The chapters discuss the challenges presented to those seeking to deploy emerging mobile technologies (e.g., smartphones, IoT, drones, robots etc.) for healthcare (mHealth) in developing countries and discuss the solutions undertaken in these case study projects.

Heart disease, including coronary artery disease (CAD) and valvular heart disease (VHD), is the leading cause of mortality and morbidity in the world. Early diagnosis is important to prevent further development of the disease. Whilst standard diagnosis methods can be highly costly and invasive, by analysing heart murmur sounds via a phonocardiogram (PCG), patterns in the heart sound characteristics can be readily detected. The intensity, timing, and duration of the heart's sounds can correspond to murmurs, valvular issues, or congenital heart defects. Sounds are recorded using electronic stethoscopes to detect abnormalities not audible to the human ear. This technology is unique in its ability to be deployed in remote and underserved areas to aid in the timely diagnosis of heart diseases.