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.

Full Product Details

Author:   David A. Clifton (Associate Professor, University of Oxford, UK)
Publisher:   Institution of Engineering and Technology
Imprint:   Institution of Engineering and Technology
ISBN:  

9781849199780

ISBN 10:   1849199787
Pages:   320
Publication Date:   28 October 2016
Audience:   College/higher education ,  Professional and scholarly ,  Tertiary & Higher Education ,  Professional & Vocational
Format:   Hardback
Publisher's Status:   Active
Availability:   In Print  
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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

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Author Information

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

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