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Novelty detection is the task of classifying test data that differ in some respect from the data that are available during training. This may be seen as " one-class classification " , in which a model is constructed to describe " normal " training data. The novelty detection approach is typically used when the quantity of available " abnormal " data is(More)
The ability to determine patient acuity (or severity of illness) has immediate practical use for clinicians. We evaluate the use of multivariate timeseries modeling with the multi-task Gaussian process (GP) models using noisy, incomplete, sparse, heterogeneous and unevenly-sampled clinical data, including both physiological signals and clinical notes. The(More)
We introduce an extreme function theory as a novel method by which probabilistic novelty detection may be performed with functions, where the functions are represented by time-series of (potentially multivariate) discrete observations. We set the method within the framework of Gaussian processes (GP), which offers a convenient means of constructing a(More)
Gaussian process (GP) models are a flexible means of performing nonparametric Bayesian regression. However, GP models in healthcare are often only used to model a single univariate output time series, denoted as single-task GPs (STGP). Due to an increasing prevalence of sensors in healthcare settings, there is an urgent need for robust multivariate(More)
Healthcare information, and to some extent patient management, is progressing toward a wireless digital future. This change is driven partly by a desire to improve the current state of medicine using new technologies, partly by supply-and-demand economics, and partly by the utility of wireless devices. Wired technology can be cumbersome for patient(More)
Novelty detection, or one-class classification, aims to determine if data are " normal " with respect to some model of normality constructed using examples of normal system behaviour. If that model is composed of generative probability distributions, the extent of " normality " in the data space can be described using Extreme Value Theory (EVT), a branch of(More)
The majority of patients in the hospital are ambulatory and would benefit significantly from predictive and personalized monitoring systems. Such patients are well suited to having their physiological condition monitored using low-power, minimally intrusive wearable sensors. Despite data-collection systems now being manufactured commercially, allowing(More)
—We present a novel method for the identification of abnormal episodes in gas-turbine vibration data, in which we show 1) how a model of normal engine behaviour is constructed using signatures of " normal " engine vibration response; 2) how extreme value theory (EVT), a branch of statistics used to determine the expected value of extreme values drawn from a(More)
Advances in wearable sensing and communications infrastructure have allowed the widespread development of prototype medical devices for patient monitoring. However, such devices have not penetrated into clinical practice, primarily due to a lack of research into "intelligent" analysis methods that are sufficiently robust to support large-scale deployment.(More)