Machine Learning for Healthcare Technologies

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.

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