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.
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.
* 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 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
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