735 research outputs found
Fusing Continuous-valued Medical Labels using a Bayesian Model
With the rapid increase in volume of time series medical data available
through wearable devices, there is a need to employ automated algorithms to
label data. Examples of labels include interventions, changes in activity (e.g.
sleep) and changes in physiology (e.g. arrhythmias). However, automated
algorithms tend to be unreliable resulting in lower quality care. Expert
annotations are scarce, expensive, and prone to significant inter- and
intra-observer variance. To address these problems, a Bayesian
Continuous-valued Label Aggregator(BCLA) is proposed to provide a reliable
estimation of label aggregation while accurately infer the precision and bias
of each algorithm. The BCLA was applied to QT interval (pro-arrhythmic
indicator) estimation from the electrocardiogram using labels from the 2006
PhysioNet/Computing in Cardiology Challenge database. It was compared to the
mean, median, and a previously proposed Expectation Maximization (EM) label
aggregation approaches. While accurately predicting each labelling algorithm's
bias and precision, the root-mean-square error of the BCLA was
11.780.63ms, significantly outperforming the best Challenge entry
(15.372.13ms) as well as the EM, mean, and median voting strategies
(14.760.52ms, 17.610.55ms, and 14.430.57ms respectively with
)
One-class classification of point patterns of extremes
Novelty detection or one-class classification starts from a model describing some type of 'normal behaviour' and aims to classify deviations from this model as being either novelties or anomalies.
In this paper the problem of novelty detection for point patterns S = {X-1 ,..., X-k} subset of R-d is treated where examples of anomalies are very sparse, or even absent. The latter complicates the tuning of hyperparameters in models commonly used for novelty detection, such as one-class support vector machines and hidden Markov models.
To this end, the use of extreme value statistics is introduced to estimate explicitly a model for the abnormal class by means of extrapolation from a statistical model X for the normal class. We show how multiple types of information obtained from any available extreme instances of S can be combined to reduce the high false-alarm rate that is typically encountered when classes are strongly imbalanced, as often occurs in the one-class setting (whereby 'abnormal' data are often scarce).
The approach is illustrated using simulated data and then a real-life application is used as an exemplar, whereby accelerometry data from epileptic seizures are analysed - these are known to be extreme and rare with respect to normal accelerometer data
Modelling physiological deterioration in post-operative patient vital-sign data
Patients who undergo upper-gastrointestinal surgery have a high incidence of post-operative complications, often requiring admission to the intensive care unit several days after surgery. A dataset comprising observational vital-sign data from 171 post-operative patients taking part in a two-phase clinical trial at the Oxford Cancer Centre, was used to explore the trajectory of patients’ vital-sign changes during their stay in the post-operative ward using both univariate and multivariate analyses. A model of normality based vital-sign data from patients who had a “normal” recovery was constructed using a kernel density estimate, and tested with “abnormal” data from patients who deteriorated sufficiently to be re-admitted to the intensive care unit. The vital-sign distributions from “normal” patients were found to vary over time from admission to the post-operative ward to their discharge home, but no significant changes in their distributions were observed from halfway through their stay on the ward to the time of discharge. The model of normality identified patient deterioration when tested with unseen “abnormal” data, suggesting that such techniques may be used to provide early warning of adverse physiological events
SoCal: Selective Oracle Questioning for Consistency-based Active Learning of Cardiac Signals
The ubiquity and rate of collection of cardiac signals produce large,
unlabelled datasets. Active learning (AL) can exploit such datasets by
incorporating human annotators (oracles) to improve generalization performance.
However, the over-reliance of existing algorithms on oracles continues to
burden physicians. To minimize this burden, we propose SoCal, a
consistency-based AL framework that dynamically determines whether to request a
label from an oracle or to generate a pseudo-label instead. We show that our
framework decreases the labelling burden while maintaining strong performance,
even in the presence of a noisy oracle
The North West Rail Link: Winners and losers in the locality of the north west area
The appraisal of large scale transport infrastructure projects by governments tends to focus on the costs and benefits to society as a whole or to broad communities affected by the project. In so doing, it is often believed by members of these communities that the greatest benefits of a scheme are likely to accrue in the immediate environs of the project. This paper examines one specific project (the NSW State Government’s proposed North West Rail Link in Metropolitan Sydney) to examine how this project impacts on different spatial areas in the environs of the project in terms of travel times and fares. Following the election of a new state government in 2011, an extension of the CityRail network into the Hills District of Sydney was announced. Known as the North-West Rail Line (NWRL), it will link Epping to Cudgegong Road beyond Rouse Hill. The project will provide rail access for the first time from the centre of the growing North West region to major employment centres in the North-West and to major centres located between the North-west and Sydney CBD. Currently there are a number of public transport options available for travel to the CBD of Sydney, the most popular being a service that operates from various locations in the NW Hills area and connects directly onto the M2 toll road; with a substantial amount of bus lane priority along its routes into the CBD of Sydney. The available information suggests existing bus services will be re-directed to the NWRL. This paper examines changes in door to door travel times for different spatial areas in the environs of the proposed new line, comparing existing services with services directed via the NWRL. The paper concludes that there are winners and losers thus challenging the belief that communities close to new infrastructure are the main beneficiaries
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