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 machine learning techniques can be applied to help individual patients.
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.
- 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 with machine learning
- 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 fibrosis detection in cardiac magnetic resonance imaging (MRI)
- Chapter 9: Enhancing physical performance with machine learning
- Chapter 10: Wearable electrochemical sensors and machine learning for real-time sweat analysis
- Chapter 11: Last words
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.
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