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Machine Learning for Healthcare Technologies

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Editor: David A. Clifton 1
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Publication Year: 2016

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.

Inspec keywords: physiological models; health care; patient monitoring; Bayes methods; learning (artificial intelligence)

Other keywords: chronic disease; patient physiological monitoring; predictive models; noisy healthcare data; Big data; genomic data; machine learning; Bayesian model; vital signs monitoring data; decision support system; ECG model-based Bayesian filtering; infectious disease model; biomedical applications; antibiotic resistance prediction

Subjects: General and management topics; Medical administration; Other topics in statistics; Biology and medical computing; Knowledge engineering techniques

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