Evaluation of an established oncology triage hotline in a model of emergency department avoidance: assessing the UKONS triage tool and call outcomes.

PURPOSE: In response to a demonstrable need for 24/7, specialist oncology advice for patients undergoing systemic anti-cancer therapy, many healthcare institutions have adopted a telephone triage (TT) service. This is true of the Clatterbridge Cancer Centre which uses the UKONS framework to guide its decisions. This study aims to investigate the utilisation and outcomes of this TT service, with a focus on the most unwell call outcomes and factors leading to referrals to accident and emergency departments that could be mitigated with service development and modifications. METHODS: A retrospective evaluation study was conducted of calls occurring between 1st September 2021 and 31st August 2022. A descriptive analysis of call UKONS grading, triage outcome and primary complaint was performed. RESULTS: The TT hotline received 23,766 calls of which only 9066 were for clinical advice. Of the clinical calls, 45.2% were UKONS red. The majority of red calls 53.3% were directed to AED. The proportion of red calls going to AED changed drastically depending on the timing of call and the corresponding services available at those times, with 38.3% of reds being sent to AED in hours but 72.3% out of hours. The profile of complaints also showed significant differences in hours versus out of hours. CONCLUSION: Significant use of the hotline supports a genuine demand for oncology TT services. In order to reduce referrals to AED, this study supports the creation of alternative destinations of emergency care, especially out of hours.

Gaussian activation functions using Markov chains.

We extend, in two major ways, earlier work in which sigmoidal neural nonlinearities were implemented using stochastic counters. 1) We define the signal to noise limitations of unipolar and bipolar stochastic arithmetic and signal processing. 2) We generalize the use of stochastic counters to include neural transfer functions employed in Gaussian mixture models. The hardware advantages of (nonlinear) stochastic signal processing (SSP) may be offset by increased processing time; we quantify these issues. The ability to realize accurate Gaussian activation functions for neurons in pulsed digital networks using simple hardware with stochastic signals is also analyzed quantitatively.

An investigation of competitive learning for autonomous cluster identification in embedded systems.

Robust signal processing for embedded systems requires the effective identification and representation of features within raw sensory data. This task is inherently difficult due to unavoidable long-term changes in the sensory systems and/or the sensed environment. In this paper we explore four variations of competitive learning and examine their suitability as an unsupervised technique for the automated identification of data clusters within a given input space. The relative performance of the four techniques is evaluated through their ability to effectively represent the structure underlying artificial and real-world data distributions. As a result of this study it was found that frequency sensitive competitive learning provides both reliable and efficient solutions to complex data distributions. As well, frequency sensitive and soft competitive learning are shown to exhibit properties which may permit the evolution of an appropriate network structure through the use of growing or pruning procedures.

Stochastic radial basis functions.

Stochastic signal processing can implement gaussian activation functions for radial basis function networks, using stochastic counters. The statistics of neural inputs which control the increment and decrement operations of the counter are governed by Bernoulli distributions. The transfer functions relating the input and output pulse probabilities can closely approximate gaussian activation functions which improve with the number of states in the counter. The means and variances of these gaussian approximations can be controlled by varying the output combinational logic function of the binary counter variables.

Vector quantization of images using modified adaptive resonance algorithm for hierarchical clustering.

A modified adaptive resonance theory (ART2) learning algorithm, which we employ in this paper, belongs to the family of NN algorithms whose main goal is the discovery of input data clusters, without considering their actual size. This feature makes the modified ART2 algorithm very convenient for image compression tasks, particularly when dealing with images with large background areas containing few details. Moreover, due to the ability to produce hierarchical quantization (clustering), the modified ART2 algorithm is proved to significantly reduce the computation time required for coding, and therefore enhance the overall compression process. Examples of the results obtained are presented, suggesting the benefits of using this algorithm for the purpose of VQ, i.e., image compression, over the other NN learning algorithms.

Compound binomial processes in neural integration.

Explores some of the properties of stochastic digital signal processing in which the input signals are represented as sequences of Bernoulli events. The event statistics of the resulting stochastic process may be governed by compound binomial processes, depending upon how the individual input variables to a neural network are stochastically multiplexed. Similar doubly stochastic statistics can also result from datasets which are Bernoulli mixtures, depending upon the temporal persistence of the mixture components at the input terminals to the network. The principal contribution of these results is in determining the required integration period of the stochastic signals for a given precision in pulsed digital neural networks.

Cooperative coevolution of neural representations.

A genetic algorithm (GA) is used to search for a set of local feature detectors or hidden units. These are in turn employed as a representation of the input data for neural learning in the upper layer of a multilayer perceptron (MLP) which performs an image classification task. Three different methods of encoding hidden unit weights in the chromosome of the GA are presented, including one which coevolves all the feature detectors in a single chromosome, and two which promote the cooperation of feature detectors by encoding them in their own individual chromosomes. The fitness function measures the MLP classification accuracy together with the confidence of the networks.

Instabilities and oscillation in the deterministic Boltzmann machine.

Simulations indicate that the deterministic Boltzmann machine, unlike the stochastic Boltzmann machine from which it is derived, exhibits unstable behavior during contrastive Hebbian learning of nonlinear problems, including oscillation in the learning algorithm and extreme sensitivity to small weight perturbations. Although careful choice of the initial weight magnitudes, the learning rate, and the annealing schedule will produce convergence in most cases, the stability of the resulting solution depends on the parameters in a complex and generally indiscernible way. We show that this unstable behavior is the result of over parameterization (excessive freedom in the weights), which leads to continuous rather than isolated optimal weight solution sets. This allows the weights to drift without correction by the learning algorithm until the free energy landscape changes in such a way that the settling procedure employed finds a different minimum of the free energy function than it did previously and a gross output error occurs. Because all the weight sets in a continuous optimal solution set produce exactly the same network outputs, we define reliability, a measure of the robustness of the network, as a new performance criterion.

Doubly stochastic Poisson processes in artificial neural learning.

This paper investigates neuron activation statistics in artificial neural networks employing stochastic arithmetic. It is shown that a doubly stochastic Poisson process is an appropriate model for the signals in these circuits.

Tolerance to analog hardware of on-chip learning in backpropagation networks.

In this paper we present results of simulations performed assuming both forward and backward computation are done on-chip using analog components. Aspects of analog hardware studied are component variability, limited voltage ranges, components (multipliers) that only approximate the computations in the backpropagation algorithm, and capacitive weight decay. It is shown that backpropagation networks can learn to compensate for all these shortcomings of analog circuits except for zero offsets, and the latter are correctable with minor circuit complications. Variability in multiplier gains is not a problem, and learning is still possible despite limited voltage ranges and function approximations. Fixed component variation from fabrication is shown to be less detrimental to learning than component variation due to noise. Weight decay is tolerable provided it is sufficiently small, which implies frequent refreshing by rehearsal on the training data or modest cooling of the circuits. The former approach allows for learning nonstationary problem sets.

Analog circuits for relaxation networks.

Selected examples are presented of recent advances, primarily from the U.S. and Canada, in analog circuits for relaxation networks. Relaxation networks having feedback connections exhibit potentially greater computational power per neuron than feedforward networks. They are also more poorly understood especially with respect to learning algorithms. Examples are described of analog circuits for (i) supervised learning in deterministic Boltzmann machines, (ii) unsupervised competitive learning and feature maps and (iii) networks with resistive grids for vision and audition tasks. We also discuss recent progress on in-circuit learning and synaptic weight storage mechanisms.

Laser angioplasty: an atherosclerotic swine model.

Rapid production of occlusive, atherosclerotic iliac artery lesions was achieved in 25 of 27 (93%) Yucatan miniature swine, using a combination of high cholesterol diet and mechanical endothelial denudation. Animals were fed a diet with 2% of their calories as raw cholesterol 2 weeks prior to balloon denudation of iliac arteries, which resulted in atherosclerotic lesions within 8 weeks. Early after denudation we have demonstrated total occlusion of arteries by fibrin thrombi, which in time organize and ultimately result in fibrotic occlusive disease. The arterial walls and intima show varying degrees of foam cell infiltration with destruction of the internal elastic lamina and calcification. Totally occluded lesions show fibrointimal proliferation, fibrosis, and multiluminal channels, which are probably secondary to organized thrombus. Our model of occlusive iliac artery disease involving vessels of 1 to 3 mm in diameter allows the development of catheter systems suitable for use in human peripheral and coronary arteries. This model is useful for the study of angioplasty, whether mechanical, balloon, or laser-mediated.

Near IR interband transitions and optical parameters of metal-germanium contacts.

A theoretical and experimental study of the zero-bias quantum efficiency eta(0) for metal (Au, Cu, Ag)-Ge Schottky barrier photodetectors in the near IR range (1.1 microm < lambda < 1.8 microm) has been performed. By an interactive computer programming technique, the optical parameters of the metal thin film electrodes (index of refraction n and extinction coefficient k) as a function of wavelength and of film thickness are determined. Starting with a two-layer calculation of the reflectance R, transmittance T, and absorptance A of the metal electrode, it is found that the eta(0) in this near IR range is dominated by the band-to-band excitation of electrons in the Ge substrate. Using the minority carrier diffusion length L(p) as an adjustable parameter, good agreement between theoretical and experimental results was found for L(p) approximately 150 microm; this value was obtained independent of choice of metal or metal thickness justifying the above procedure.

Photocounting distributions for exponentially decaying sources.

Exact photocounting distributions are obtained for a pulse of light whose intensity is exponentially decaying in time, when the underlying photon statistics are Poisson. It is assumed that the starting time for the sampling interval (which is of arbitrary duration) is uniformly distributed. The probability of registering n counts in the fixed time T is given in terms of the incomplete gamma function for n >/= 1 and in terms of the exponential integral for n = 0. Simple closed-form expressions are obtained for the count mean and variance. The results are expected to be of interest in certain studies involving spontaneous emission, radiation damage in solids, and nuclear counting. They will also be useful in neurobiology and psychophysics, since habituation and sensitization processes may sometimes be characterized by the same stochastic model.