Main 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.

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