Machine Learning for Healthcare Technologies

by David A. Clifton

Overview

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.

Product Details

ISBN-13:	9781849199780
Publisher:	The Institution of Engineering and Technology
Publication date:	10/28/2016
Series:	Healthcare Technologies
Pages:	320
Product dimensions:	6.14(w) x 9.21(h) x (d)

About the Author

David Clifton is Associate Professor of Engineering Science at the Universityof 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.

Table of Contents

• Chapter 1: Machine learning for healthcare technologies - an introduction
• Chapter 2: Detecting artifactual events in vital signs monitoring data
• Chapter 3: Signal processing and feature selection preprocessing for classification in noisy healthcare data
• Chapter 4: ECG model-based Bayesian filtering
• Chapter 5: The power of tensor decompositions in biomedical applications
• Chapter 6: Patient physiological monitoring with machine learning
• Chapter 7: A Bayesian model for fusing biomedical labels
• Chapter 8: Incorporating end-user preferences in predictive models
• Chapter 9: Variational Bayesian non-parametric inference for infectious disease models
• Chapter 10: Predicting antibiotic resistance from genomic data
• Chapter 11: Machine learning for chronic disease
• Chapter 12: Big data and optimisation of treatment strategies
• Chapter 13: Decision support systems for home monitoring applications: Classification of activities of daily living and epileptic seizures