Measurement of magnetostriction using dual laser heterodyne interferometers : experimental challenges and preliminary results

Vibrations and noise of electrical machines and transformers may be caused by Lorentz forces and/or by magnetostriction. Here we only focus on the vibrations and noise due to magnetostriction. Electrical machines and transformers have magnetic cores of ferromagnetic material. Magnetostriction can be seen as a reaction of the ferromagnetic material to the presence of a magnetic field in the material and it leads to unwanted noise. The magnetostriction varies from material to material and is dependent on the magnetic field (or the magnetic induction) and on external stresses applied to the material. For every different material, the magnetostriction properties have to be obtained experimentally, usually by means of magnetostriction strain measurements. In the past a measurement set-up using strain gauges was developed at the Electrical Energy Laboratory (EELAB). In this paper a new magnetostriction measurement set-up using a dual laser heterodyne interferometer is proposed which avoids the drawbacks of the strain gauge set-up. The preliminary measurements already show some promising results. The experimental challenges and future work are explained at the end

A trial-based cost-utility analysis of metastasis-directed therapy for oligorecurrent prostate cancer

The optimal management of patients with oligorecurrent prostate cancer (PCa) is unknown. There is growing interest in metastasis-directed therapy (MDT) for this population. The objective was to assess cost-utility from a Belgian healthcare payer's perspective of MDT and delayed androgen deprivation therapy (ADT) in comparison with surveillance and delayed ADT, and with immediate ADT. A Markov decision-analytic trial-based model was developed, projecting the results over a 5-year time horizon with one-month cycles. Clinical data were derived from the STOMP trial and literature. Treatment costs were derived from official government documents. Probabilistic sensitivity analyses showed that MDT is cost-effective compared to surveillance (ICER: Euro8393/quality adjusted life year (QALY)) and immediate ADT (dominant strategy). The ICER is most sensitive to utilities in the different health states and the first month MDT cost. At a willingness-to-pay threshold of Euro40,000 per QALY, the cost of the first month MDT should not exceed Euro8136 to be cost-effective compared to surveillance. The Markov-model suggests that MDT for oligorecurrent PCa is potentially cost-effective in comparison with surveillance and delayed ADT, and in comparison with immediate ADT

Designing the selenium and bladder cancer trial (SELEBLAT), a phase lll randomized chemoprevention study with selenium on recurrence of bladder cancer in Belgium

<p>Abstract</p> <p>Background</p> <p>In Belgium, bladder cancer is the fifth most common cancer in males (5.2%) and the sixth most frequent cause of death from cancer in males (3.8%). Previous epidemiological studies have consistently reported that selenium concentrations were inversely associated with the risk of bladder cancer. This suggests that selenium may also be suitable for chemoprevention of recurrence.</p> <p>Method</p> <p>The SELEBLAT study opened in September 2009 and is still recruiting all patients with non-invasive transitional cell carcinoma of the bladder on TURB operation in 15 Belgian hospitals. Recruitment progress can be monitored live at <url>http://www.seleblat.org.</url> Patients are randomly assigned to selenium yeast (200 μg/day) supplementation for 3 years or matching placebo, in addition to standard care. The objective is to determine the effect of selenium on the recurrence of bladder cancer. Randomization is stratified by treatment centre. A computerized algorithm randomly assigns the patients to a treatment arm. All study personnel and participants are blinded to treatment assignment for the duration of the study.</p> <p>Design</p> <p>The SELEnium and BLAdder cancer Trial (SELEBLAT) is a phase III randomized, placebo-controlled, academic, double-blind superior trial.</p> <p>Discussion</p> <p>This is the first report on a selenium randomized trial in bladder cancer patients.</p> <p>Trial registration</p> <p>ClinicalTrials.gov identifier: <a href="http://www.clinicaltrials.gov/ct2/show/NCT00729287">NCT00729287</a></p

Interval Coded Scoring: Towards Data-derived Medical Scoring Systems

status: publishe

Activity Recognition for Physical Therapy: Moving to the Home Environment

status: publishe

Interval Coded Scoring Index with Interaction Effects: a Sensitivity Study

© Copyright 2016 by SCITEPRESS - Science and Technology Publications, Lda. All rights reserved. Scoring systems have been used since long in medical practice, but often they are based on experience rather than a structural approach. In literature, the interval coded scoring index (ICS) has been introduced as an alternative. It derives a scoring system from data using optimization techniques. This work discusses an extension, ICS∗, that takes variable interactions into account. Furthermore, a study is performed to give insight into the new model's sensitivity to noise, the size of the data set and the number of non-informative variables. The study shows interactions can mostly be discovered robustly, even in the presence of noise and spurious variables. A final validation on two UCI data sets further indicates the quality of the approach.status: publishe

Zijn er nog Belgen? Twintig jaar enquêtes over politiek-territoriale identiteiten

status: publishe

Interval coded scoring : a toolbox for interpretable scoring systems

© 2018 Billiet et al. Over the last decades, clinical decision support systems have been gaining importance. They help clinicians to make effective use of the overload of available information to obtain correct diagnoses and appropriate treatments. However, their power often comes at the cost of a black box model which cannot be interpreted easily. This interpretability is of paramount importance in a medical setting with regard to trust and (legal) responsibility. In contrast, existing medical scoring systems are easy to understand and use, but they are often a simplified rule-of-thumb summary of previous medical experience rather than a well-founded system based on available data. Interval Coded Scoring (ICS) connects these two approaches, exploiting the power of sparse optimization to derive scoring systems from training data. The presented toolbox interface makes this theory easily applicable to both small and large datasets. It contains two possible problem formulations based on linear programming or elastic net. Both allow to construct a model for a binary classification problem and establish risk profiles that can be used for future diagnosis. All of this requires only a few lines of code. ICS differs from standard machine learning through its model consisting of interpretable main effects and interactions. Furthermore, insertion of expert knowledge is possible because the training can be semi-automatic. This allows end users to make a tradeoff between complexity and performance based on cross-validation results and expert knowledge. Additionally, the toolbox offers an accessible way to assess classification performance via accuracy and the ROC curve, whereas the calibration of the risk profile can be evaluated via a calibration curve. Finally, the colour-coded model visualization has particular appeal if one wants to apply ICS manually on new observations, as well as for validation by experts in the specific application domains. The validity and applicability of the toolbox is demonstrated by comparing it to standard Machine Learning approaches such as Naive Bayes and Support Vector Machines for several reallife datasets. These case studies on medical problems show its applicability as a decision support system. ICS performs similarly in terms of classification and calibration. Its slightly lower performance is countered by its model simplicity which makes it the method of choice if interpretability is a key issue.status: publishe

Interval Coded Scoring: a toolbox for interpretable scoring systems

Over the last decades, clinical decision support systems have been gaining importance. They help clinicians to make effective use of the overload of available information to obtain correct diagnoses and appropriate treatments. However, their power often comes at the cost of a black box model which cannot be interpreted easily. This interpretability is of paramount importance in a medical setting with regard to trust and (legal) responsibility. In contrast, existing medical scoring systems are easy to understand and use, but they are often a simplified rule-of-thumb summary of previous medical experience rather than a well-founded system based on available data. Interval Coded Scoring (ICS) connects these two approaches, exploiting the power of sparse optimization to derive scoring systems from training data. The presented toolbox interface makes this theory easily applicable to both small and large datasets. It contains two possible problem formulations based on linear programming or elastic net. Both allow to construct a model for a binary classification problem and establish risk profiles that can be used for future diagnosis. All of this requires only a few lines of code. ICS differs from standard machine learning through its model consisting of interpretable main effects and interactions. Furthermore, insertion of expert knowledge is possible because the training can be semi-automatic. This allows end users to make a trade-off between complexity and performance based on cross-validation results and expert knowledge. Additionally, the toolbox offers an accessible way to assess classification performance via accuracy and the ROC curve, whereas the calibration of the risk profile can be evaluated via a calibration curve. Finally, the colour-coded model visualization has particular appeal if one wants to apply ICS manually on new observations, as well as for validation by experts in the specific application domains. The validity and applicability of the toolbox is demonstrated by comparing it to standard Machine Learning approaches such as Naive Bayes and Support Vector Machines for several real-life datasets. These case studies on medical problems show its applicability as a decision support system. ICS performs similarly in terms of classification and calibration. Its slightly lower performance is countered by its model simplicity which makes it the method of choice if interpretability is a key issue