System identification using partitioned least squares

A novel partitioned least squares (PLS) algorithm is presented, in which estimates from several simple system models are combined by means of a Bayesian methodology of pooling partial knowledge. The method has the added advantage that, when the simple models are of a similar structure, it lends itself directly to parallel processing procedures, thereby speeding up the entire parameter estimation process by several factors.

Nonlinear model structure design and construction using orthogonal least squares and D-optimality design

A very efficient learning algorithm for model subset selection is introduced based on a new composite cost function that simultaneously optimizes the model approximation ability and model robustness and adequacy. The derived model parameters are estimated via forward orthogonal least squares, but the model subset selection cost function includes a D-optimality design criterion that maximizes the determinant of the design matrix of the subset to ensure the model robustness, adequacy, and parsimony of the final model. The proposed approach is based on the forward orthogonal least square (OLS) algorithm, such that new D-optimality-based cost function is constructed based on the orthogonalization process to gain computational advantages and hence to maintain the inherent advantage of computational efficiency associated with the conventional forward OLS approach. Illustrative examples are included to demonstrate the effectiveness of the new approach.

Structural matching of 2D electrophoresis gels using deformed graphs

2D electrophoresis is a well-known method for protein separation which is extremely useful in the field of proteomics. Each spot in the image represents a protein accumulation and the goal is to perform a differential analysis between pairs of images to study changes in protein content. It is thus necessary to register two images by finding spot correspondences. Although it may seem a simple task, generally, the manual processing of this kind of images is very cumbersome, especially when strong variations between corresponding sets of spots are expected (e.g. strong non-linear deformations and outliers). In order to solve this problem, this paper proposes a new quadratic assignment formulation together with a correspondence estimation algorithm based on graph matching which takes into account the structural information between the detected spots. Each image is represented by a graph and the task is to find a maximum common subgraph. Successful experimental results using real data are presented, including an extensive comparative performance evaluation with ground-truth data. (C) 2010 Elsevier B.V. All rights reserved.

Using the EM Algorithm to Train Neural Networks: Misconceptions and a New Algorithm for Multiclass Classification

The expectation-maximization (EM) algorithm has been of considerable interest in recent years as the basis for various algorithms in application areas of neural networks such as pattern recognition. However, there exists some misconceptions concerning its application to neural networks. In this paper, we clarify these misconceptions and consider how the EM algorithm can be adopted to train multilayer perceptron (MLP) and mixture of experts (ME) networks in applications to multiclass classification. We identify some situations where the application of the EM algorithm to train MLP networks may be of limited value and discuss some ways of handling the difficulties. For ME networks, it is reported in the literature that networks trained by the EM algorithm using iteratively reweighted least squares (IRLS) algorithm in the inner loop of the M-step, often performed poorly in multiclass classification. However, we found that the convergence of the IRLS algorithm is stable and that the log likelihood is monotonic increasing when a learning rate smaller than one is adopted. Also, we propose the use of an expectation-conditional maximization (ECM) algorithm to train ME networks. Its performance is demonstrated to be superior to the IRLS algorithm on some simulated and real data sets.

Improving the tracking capability of adaptive filters via convex combination

As is well known, Hessian-based adaptive filters (such as the recursive-least squares algorithm (RLS) for supervised adaptive filtering, or the Shalvi-Weinstein algorithm (SWA) for blind equalization) converge much faster than gradient-based algorithms [such as the least-mean-squares algorithm (LMS) or the constant-modulus algorithm (CMA)]. However, when the problem is tracking a time-variant filter, the issue is not so clear-cut: there are environments for which each family presents better performance. Given this, we propose the use of a convex combination of algorithms of different families to obtain an algorithm with superior tracking capability. We show the potential of this combination and provide a unified theoretical model for the steady-state excess mean-square error for convex combinations of gradient- and Hessian-based algorithms, assuming a random-walk model for the parameter variations. The proposed model is valid for algorithms of the same or different families, and for supervised (LMS and RLS) or blind (CMA and SWA) algorithms.

Percutaneous vertebroplasty for multiple myeloma of the cervical spine

Spinal involvement is a common presentation of multiple myeloma (MM); however, the cervical spine is the least common site of myelomatous involvement. Few studies evaluate the results of percutaneous vertebroplasty (PV) in the treatment of MM of the spine. The purpose of this series is to report on the use of PV in the treatment of MM of the cervical spine and to review the literature. From January 1994 to October 2007, four patients (three men and one woman; mean age, 45 years) who underwent five PV for painful MM in the cervical spine were retrospectively reviewed. The pain was estimated by the patient on a verbal analogic scale. Clinical follow-up was available for all patients (mean, 27.5 months; range, 1-96 months). The mean volume of cement injected per vertebral body was 2.3 +/- 0.8 mL (range, 1.0-4.0 mL) with a mean vertebral filling of 55.0 +/- 12.0% (range, 40.0-75.0%). Analgesic efficacy was achieved in all patients. One patient had a spinal instability due to a progression of spinal deformity noted on follow-up radiographs, without clinical symptoms. Cement leakage was detected in three (60%) of the five treated vertebrae. There was no clinical complication. The present series suggests that PV for MM of the cervical spine is safe and effective for pain control; nonetheless, the detrimental impact of the disease on bone quality should prompt close radiological follow-up after PV owing to the risk of spinal instability.

La planification de sortie des personnes âgées hospitalisées dans un service de médecine

Contexte: la planification infirmière de sortie des personnes âgées est une composante importante des soins pour assurer une transition optimale entre l'hôpital et la maison. Beaucoup d'événements indésirables peuvent survenir après la sortie de l'hôpital. Dans une perspective de système de santé, les facteurs qui augmentent ce risque incluent un nombre croissant de patients âgés, l'augmentation de la complexité des soins nécessitant une meilleure coordination des soins après la sortie, ainsi qu'une augmentation de la pression financière. Objectif: évaluer si les interventions infirmières liées à la planification de sortie chez les personnes âgées et leurs proches aidants sont prédictives de leur perception d'être prêts pour le départ, du niveau d'anxiété du patient le jour de la sortie de l'hôpital et du nombre de recours non programmé aux services de santé durant les trente jours après la sortie. Méthode: le devis est prédictif corrélationnel avec un échantillon de convenance de 235 patients. Les patients âgés de 65 ans de quatre unités d'hôpitaux dans le canton de Vaud en Suisse ont été recrutés entre novembre 2011 et octobre 2012. Les types et les niveaux d'interventions infirmières ont été extraits des dossiers de soins et analysés selon les composantes du modèle de Naylor. La perception d'être prêt pour la sortie et l'anxiété ont été mesurées un jour avant la sortie en utilisant l'échelle de perception d'être prêt pour la sortie et l'échelle Hospital Anxiety and Depression. Un mois après la sortie, un entretien téléphonique a été mené pour évaluer le recours non programmé aux services de santé durant cette période. Des analyses descriptives et un modèle randomisé à deux niveaux ont été utilisés pour analyser les données. Résultats: peu de patients ont reçu une planification globale de sortie. L'intervention la plus fréquente était la coordination (M = 55,0/100). et la moins fréquente était la participation du patient à la planification de sortie (M = 16,1/100). Contrairement aux hypothèses formulées, les patients ayant bénéficié d'un plus grand nombre d'interventions infirmières de préparation à la sortie ont un niveau moins élevé de perception d'être prêt pour le départ (B = -0,3, p < 0,05, IC 95% [-0,57, -0,11]); le niveau d'anxiété n'est pas associé à la planification de sortie (r = -0,21, p <0,01) et la présence de troubles cognitifs est le seul facteur prédictif d'une réhospitalisation dans les 30 jours après la sortie de l'hôpital ( OR = 1,50, p = 0,04, IC 95% [1,02, 2,22]). Discussion: en se focalisant sur chaque intervention de la planification de sortie, cette étude permet une meilleure compréhension du processus de soins infirmiers actuellement en cours dans les hôpitaux vaudois. Elle met en lumière les lacunes entre les pratiques actuelles et celles de pratiques exemplaires donnant ainsi une orientation pour des changements dans la pratique clinique et des recherches ultérieures. - Background: Nursing discharge planning in elderly patients is an important component of care to ensure optimal transition from hospital to home. Many adverse events may occur after hospital discharge. From a health care system perspective, contributing factors that increase the risk of these adverse events include a growing number of elderly patients, increased complexity of care requiring better care coordination after discharge, as well as increased financial pressure. Aim: To investigate whether older medical inpatients who receive comprehensive discharge planning interventions a) feel more ready for hospital discharge, b) have reduced anxiety at the time of discharge, c) have lower health care utilization after discharge compared to those who receive less comprehensive interventions. Methods: Using a predictive correlational design, a convenience sample of 235 patients was recruited. Patients aged 65 and older from 4 units of hospitals in the canton of Vaud in Switzerland were enrolled between November 2011 and October 2012. Types and level of interventions were extracted from the medical charts and analyzed according to the components of Naylor's model. Discharge readiness and anxiety were measured one day before discharge using the Readiness for Hospital Discharge Scale and the Hospital Anxiety and Depression scale. A telephone interview was conducted one month after hospital discharge to asses unplanned health services utilization during this follow-up period. Descriptive analyses and a two- level random model were used for statistical analyses. Results: Few patients received comprehensive discharge planning interventions. The most frequent intervention was Coordination (M = 55,0/100) and the least common was Patient participation in the discharge planning (M = 16,1/100). Contrary to our hypotheses, patients who received more nursing discharge interventions were significantly less ready to go home (B = -0,3, p < 0,05, IC 95% [-0,57, -0,11]); their anxiety level was not associated with their readiness for hospital discharge (r = -0,21, p <0,01) and cognitive impairment was the only factor that predicted rehospitalization within 30 days after discharge ( OR = 1,50, p = 0,04, IC 95% [1,02, 2,22]). Discussion: By focusing on each component of the discharge planning, this study provides a greater and more detailed insight on the usual nursing process currently performed in medical inpatients units. Results identified several gaps between current and Best practices, providing guidance to changes in clinical practice and further research.

Développement et validation d'une méthode de calcul indépendant des unités moniteur basée sur les simulations Monte Carlo pour la vérification des traitements IMRT

Résumé : La radiothérapie par modulation d'intensité (IMRT) est une technique de traitement qui utilise des faisceaux dont la fluence de rayonnement est modulée. L'IMRT, largement utilisée dans les pays industrialisés, permet d'atteindre une meilleure homogénéité de la dose à l'intérieur du volume cible et de réduire la dose aux organes à risque. Une méthode usuelle pour réaliser pratiquement la modulation des faisceaux est de sommer de petits faisceaux (segments) qui ont la même incidence. Cette technique est appelée IMRT step-and-shoot. Dans le contexte clinique, il est nécessaire de vérifier les plans de traitement des patients avant la première irradiation. Cette question n'est toujours pas résolue de manière satisfaisante. En effet, un calcul indépendant des unités moniteur (représentatif de la pondération des chaque segment) ne peut pas être réalisé pour les traitements IMRT step-and-shoot, car les poids des segments ne sont pas connus à priori, mais calculés au moment de la planification inverse. Par ailleurs, la vérification des plans de traitement par comparaison avec des mesures prend du temps et ne restitue pas la géométrie exacte du traitement. Dans ce travail, une méthode indépendante de calcul des plans de traitement IMRT step-and-shoot est décrite. Cette méthode est basée sur le code Monte Carlo EGSnrc/BEAMnrc, dont la modélisation de la tête de l'accélérateur linéaire a été validée dans une large gamme de situations. Les segments d'un plan de traitement IMRT sont simulés individuellement dans la géométrie exacte du traitement. Ensuite, les distributions de dose sont converties en dose absorbée dans l'eau par unité moniteur. La dose totale du traitement dans chaque élément de volume du patient (voxel) peut être exprimée comme une équation matricielle linéaire des unités moniteur et de la dose par unité moniteur de chacun des faisceaux. La résolution de cette équation est effectuée par l'inversion d'une matrice à l'aide de l'algorithme dit Non-Negative Least Square fit (NNLS). L'ensemble des voxels contenus dans le volume patient ne pouvant être utilisés dans le calcul pour des raisons de limitations informatiques, plusieurs possibilités de sélection ont été testées. Le meilleur choix consiste à utiliser les voxels contenus dans le Volume Cible de Planification (PTV). La méthode proposée dans ce travail a été testée avec huit cas cliniques représentatifs des traitements habituels de radiothérapie. Les unités moniteur obtenues conduisent à des distributions de dose globale cliniquement équivalentes à celles issues du logiciel de planification des traitements. Ainsi, cette méthode indépendante de calcul des unités moniteur pour l'IMRT step-andshootest validée pour une utilisation clinique. Par analogie, il serait possible d'envisager d'appliquer une méthode similaire pour d'autres modalités de traitement comme par exemple la tomothérapie. Abstract : Intensity Modulated RadioTherapy (IMRT) is a treatment technique that uses modulated beam fluence. IMRT is now widespread in more advanced countries, due to its improvement of dose conformation around target volume, and its ability to lower doses to organs at risk in complex clinical cases. One way to carry out beam modulation is to sum smaller beams (beamlets) with the same incidence. This technique is called step-and-shoot IMRT. In a clinical context, it is necessary to verify treatment plans before the first irradiation. IMRT Plan verification is still an issue for this technique. Independent monitor unit calculation (representative of the weight of each beamlet) can indeed not be performed for IMRT step-and-shoot, because beamlet weights are not known a priori, but calculated by inverse planning. Besides, treatment plan verification by comparison with measured data is time consuming and performed in a simple geometry, usually in a cubic water phantom with all machine angles set to zero. In this work, an independent method for monitor unit calculation for step-and-shoot IMRT is described. This method is based on the Monte Carlo code EGSnrc/BEAMnrc. The Monte Carlo model of the head of the linear accelerator is validated by comparison of simulated and measured dose distributions in a large range of situations. The beamlets of an IMRT treatment plan are calculated individually by Monte Carlo, in the exact geometry of the treatment. Then, the dose distributions of the beamlets are converted in absorbed dose to water per monitor unit. The dose of the whole treatment in each volume element (voxel) can be expressed through a linear matrix equation of the monitor units and dose per monitor unit of every beamlets. This equation is solved by a Non-Negative Least Sqvare fif algorithm (NNLS). However, not every voxels inside the patient volume can be used in order to solve this equation, because of computer limitations. Several ways of voxel selection have been tested and the best choice consists in using voxels inside the Planning Target Volume (PTV). The method presented in this work was tested with eight clinical cases, which were representative of usual radiotherapy treatments. The monitor units obtained lead to clinically equivalent global dose distributions. Thus, this independent monitor unit calculation method for step-and-shoot IMRT is validated and can therefore be used in a clinical routine. It would be possible to consider applying a similar method for other treatment modalities, such as for instance tomotherapy or volumetric modulated arc therapy.

Intelligent Internet Data Searching

In this thesis author approaches the problem of automated text classification, which is one of basic tasks for building Intelligent Internet Search Agent. The work discusses various approaches to solving sub-problems of automated text classification, such as feature extraction and machine learning on text sources. Author also describes her own multiword approach to feature extraction and pres-ents the results of testing this approach using linear discriminant analysis based classifier, and classifier combining unsupervised learning for etalon extraction with supervised learning using common backpropagation algorithm for multilevel perceptron.

Nursing discharge planning for older medical inpatients in Switzerland: A cross-sectional study.

Nursing discharge planning for elderly medical inpatients is an essential element of care to ensure optimal transition to home and to reduce post-discharge adverse events. The objectives of this cross-sectional study were to investigate the association between nursing discharge planning components in older medical inpatients, patients' readiness for hospital discharge and unplanned health care utilization during the following 30 days. Results indicated that no patients benefited from comprehensive discharge planning but most benefited from less than half of the discharge planning components. The most frequent intervention recorded was coordination, and the least common was patients' participation in decisions regarding discharge. Patients who received more nursing discharge components felt significantly less ready to go home and had significantly more readmissions during the 30-day follow-up period. This study highlights large gaps in the nursing discharge planning process in older medical inpatients and identifies specific areas where improvements are most needed.

Sleep habits and complaints of adults in the city of São Paulo, Brazil, in 1987 and 1995

This study compares the prevalence of complaints of insomnia, excessive diurnal sleepiness, parasomnias, and sleep habits of the adult population in the city of São Paulo, Brazil, estimated in surveys carried out in 1987 and 1995. Representative samples of 1000 adult residents per survey were interviewed using a validated structured sleep questionnaire, the "UNIFESP Sleep Questionnaire". Difficulty maintaining sleep, difficulty initiating sleep and early morning awakening, occurring at least three times a week, were reported in 1987 and 1995, by 15.8/27.6, 13.9/19.1, and 10.6/14.2% of the interviewees, respectively, significantly increasing throughout time. These sleep problems were more often found among women. Frequencies of excessive diurnal sleepiness and sleep attacks were unchanged comparing 1987 with 1995 (4.5 vs 3.8 and 3.1 vs 3.0%, respectively). Parasomnia complaints remained unchanged, with the exception of leg cramps, which doubled in prevalence from 1987 to 1995 (2.6 to 5.8%). Snoring was the most common parasomnia (21.5% in 1995), reported more often by men than by women, and somnambulism was the least common (approximately 1%). Besides sleeping slightly less, interviewees went to bed and woke up later in 1995. Approximately 12% of the subjects in both surveys had consulted a physician due to sleep problems and 3.0% reported habitual use of sleep-promoting substances in 1995. Overall, there was a significant increase in insomnia complaints from 1987 to 1995 in the general population of the city of São Paulo. This major change over a little under a decade should be considered as an important public health issue.

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M-estimator, and D-optimality model construction using orthogonal forward regression

This correspondence introduces a new orthogonal forward regression (OFR) model identification algorithm using D-optimality for model structure selection and is based on an M-estimators of parameter estimates. M-estimator is a classical robust parameter estimation technique to tackle bad data conditions such as outliers. Computationally, The M-estimator can be derived using an iterative reweighted least squares (IRLS) algorithm. D-optimality is a model structure robustness criterion in experimental design to tackle ill-conditioning in model Structure. The orthogonal forward regression (OFR), often based on the modified Gram-Schmidt procedure, is an efficient method incorporating structure selection and parameter estimation simultaneously. The basic idea of the proposed approach is to incorporate an IRLS inner loop into the modified Gram-Schmidt procedure. In this manner, the OFR algorithm for parsimonious model structure determination is extended to bad data conditions with improved performance via the derivation of parameter M-estimators with inherent robustness to outliers. Numerical examples are included to demonstrate the effectiveness of the proposed algorithm.

Experimental design and model construction algorithms for radial basis function networks

New construction algorithms for radial basis function (RBF) network modelling are introduced based on the A-optimality and D-optimality experimental design criteria respectively. We utilize new cost functions, based on experimental design criteria, for model selection that simultaneously optimizes model approximation, parameter variance (A-optimality) or model robustness (D-optimality). The proposed approaches are based on the forward orthogonal least-squares (OLS) algorithm, such that the new A-optimality- and D-optimality-based cost functions are constructed on the basis of an orthogonalization process that gains computational advantages and hence maintains the inherent computational efficiency associated with the conventional forward OLS approach. The proposed approach enhances the very popular forward OLS-algorithm-based RBF model construction method since the resultant RBF models are constructed in a manner that the system dynamics approximation capability, model adequacy and robustness are optimized simultaneously. The numerical examples provided show significant improvement based on the D-optimality design criterion, demonstrating that there is significant room for improvement in modelling via the popular RBF neural network.