Machine Learning for Healthcare Technologies

Edited by David A. Clifton
Cloth: 978 1 84919 978 0 / $150.00
 
Published: November 2016  

Publisher: The Institution of Engineering and Technology
296 pp., 6 1/8" x 9 1/5"
This book brings together chapters on the state-of-the-art in machine learning (ML) as it applies to the development of patient-centred technologies, with a special emphasis on “big data” and mobile data. With contributions from international experts from prestigious institutions it describes cutting edge research and makes accessible, for the first time, the latest in Bayesian non-parametrics for healthcare. This is one of the key frontiers in ML, and its application to healthcare will serve as a useful tutorial guide for both ML-focused and biomedical engineers. There are very few books that are accessible in this key area of ML, and absolutely none on the use of such technologies for mobile healthcare – despite a substantial amount of research that has taken place in this field at key biomedical and clinical sites across the world.

Topics covered include an introduction to machine learning in healthcare; discovering trends in patient physiology; Bayesian time-series analysis for patient monitoring; mobile healthcare for the developing world; massively-multiscale machine learning for healthcare; time-series clustering for understanding patient data; machine learning for home healthcare; fusing genomics and healthcare data; machine learning for mental health; mobile healthcare with analysis-on-a-chip; Bayesian analytics for medical data fusion.

This is an important book for academic and industrial researchers working in healthcare technologies, biomedical engineering and machine learning. It will also be of interest to advanced students working in these areas and commercial developers of computing-based healthcare applications.

Table of Contents:
1. Machine learning for healthcare technologies – an introduction
David A. Clifton
2. Detecting artifactual events in vital signs monitoring data
Partha Lal, Christopher K. I.Williams, Konstantinos Georgatzis, Christopher Hawthorne, Paul McMonagle, Ian Piper and Martin Shaw
3. Signal processing and feature selection preprocessing for classification in noisy healthcare data
Qiao Li, Chengyu Liu, Julien Oster and Gari D. Clifford
4. ECG model-based Bayesian filtering
Julien Oster
5. The power of tensor decompositions in biomedical applications
Borbála Hunyadi, Sabine Van Huffel and Maarten De Vos
6. Patient physiological monitoring with machine learning
Marco A. F. Pimentel and David A. Clifton
7. A Bayesian model for fusing biomedical labels
Tingting Zhu, Gari D. Clifford and David A. Clifton
8. Incorporating end-user preferences in predictive models
Suchi Saria and Daniel P. Robinson
9. Variational Bayesian non-parametric inference for infectious disease models
James Hensman and Theodore Kypraios
10. Predicting antibiotic resistance from genomic data
Yang Yang, Katherine E. Niehaus and David A. Clifton
11. Machine learning for chronic disease
Katherine E. Niehaus and David A. Clifton
12. Big data and optimisation of treatment strategies
Shamim Nemati and Mohammad M. Ghassemi
13. Decision support systems for home monitoring applications: Classification of activities of daily living and epileptic seizures
Stijn Luca, Lode Vuegen, Hugo Van hamme, Peter Karsmakers and Bart Vanrumstehome
Index