Main description:

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.

Contents:

Chapter 1: Introduction
Chapter 2: Health System Planning and Optimisation: Advancements in the application of Machine Learning to Policy Decisions in Global Health
Chapter 3: Health System Preparedness - Coordination and Sharing of Computation, Models and Data
Chapter 4: Applications of Machine Learning for Image-Guided Microsurgery
Chapter 5: Electrophysiology and Consciousness: A Review
Chapter 6: Brain Networking and Early Diagnosis of Alzheimer's Disease
Chapter 7: From Classic Machine Learning to Deep Learning Advances in Atrial Fibrillation Detection
Chapter 8: Dictionary Learning techniques for Left Ventricle (LV) analysis and brosis detection in cardiac Magnetic Resonance Imaging (MRI)
Chapter 9: Enhancing Physical Performance with Machine Learning
Chapter 10: CMOS-Based Wearable Electrochemical Sensors for Real-Time Sweat Analysis
Chapter 11: Last words