Machine Learning for Healthcare Technologies
Published on 28. October 2016
Book
Hardback
320 pages
978-1-84919-978-0 (ISBN)
Description
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.
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.
More details
Series
Language
English
Place of publication
Stevenage
United Kingdom
Target group
College/higher education
Professional and scholarly
Product notice
sewn/stitched
Cloth over boards
Dimensions
Height: 239 mm
Width: 163 mm
Thickness: 20 mm
Weight
590 gr
ISBN-13
978-1-84919-978-0 (9781849199780)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Person
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.
Content
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
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