Revisiting Youden's index as a useful measure of the misclassification error in meta-analysis of diagnostic studies

The paper considers meta-analysis of diagnostic studies that use a continuous Score for classification of study participants into healthy, or diseased groups. Classification is often done on the basis of a threshold or cut-off value, which might vary between Studies. Consequently, conventional meta-analysis methodology focusing solely on separate analysis of sensitivity and specificity might he confounded by a potentially unknown variation of the cut-off Value. To cope with this phenomena it is suggested to use, instead an overall estimate of the misclassification error previously suggested and used as Youden's index and; furthermore, it is argued that this index is less prone to between-study variation of cut-off values. A simple Mantel-Haenszel estimator as a summary measure of the overall misclassification error is suggested, which adjusts for a potential study effect. The measure of the misclassification error based on Youden's index is advantageous in that it easily allows an extension to a likelihood approach, which is then able to cope with unobserved heterogeneity via a nonparametric mixture model. All methods are illustrated at hand of an example on a diagnostic meta-analysis on duplex doppler ultrasound, with angiography as the standard for stroke prevention.

Web-based occupant feedback for building energy management systems using fuzzy logic

This paper describes the development and validation of a novel web-based interface for the gathering of feedback from building occupants about their environmental discomfort including signs of Sick Building Syndrome (SBS). The gathering of such feedback may enable better targeting of environmental discomfort down to the individual as well as the early detection and subsequently resolution by building services of more complex issues such as SBS. The occupant's discomfort is interpreted and converted to air-conditioning system set points using Fuzzy Logic. Experimental results from a multi-zone air-conditioning test rig have been included in this paper.

Functional petit-suisse cheese: measure of the prebiotic effect

Prebiotics and probiotics are increasingly being used to produce potentially synbiotic foods, particularly through dairy products as vehicles. It is well known that both ingredients may offer benefits to improve the host health. This research aimed to evaluate the prebiotic potential of novel petit-suisse cheeses using an in vitro fermentation model. Five petit-suisse cheese formulations combining candidate prebiotics (inulin. oligofructose. hone) and probiotics (Lactobacillus acidophilus, Bifidobacterium lactis) were tested in vitro using, sterile. stirred, batch culture fermentations with human faecal slurry. Measurement of prebiotic effect (MPE) values were generated comparing bacterial changes through determination of maximum growth rates of groups, rate of substrate assimilation and production of lactate and short chain fatty acids. Fastest fermentation and high lactic acid production, promoting increased growth rates of bifidobacteria and lactobacilli. were achieved with addition of prebiotics to a probiotic cheese (made using starter + probiotics). Addition of probiotic strains to control cheese (made using just a starter culture) also resulted in high lactic acid production. Highest MPE values were obtained with addition of prebiotics to a probiotic cheese, followed by addition of prebiotics and/or probiotics to a control cheese. Under the in vitro conditions used, cheese made with the combination of different prebiotics and probiotics resulted in the most promising functional petit-suisse cheese. The study allowed comparison of potentially functional petit-suisse cheeses and screening of preferred synbiotic potential for future market use. (c) 2007 Elsevier Ltd. All rights reserved.

Family interventions in psychosis: a scale to measure therapist adherence

The efficacy of family interventions in psychosis is well documented. UK and USA schizophrenia treatment guidelines advocate the practice of family interventions within routine clinical services. However, less attention has been paid to the study of treatment fidelity and the tools used in its assessment. This study reports the inter-rater reliability of a new scale: Family Intervention in Psychosis-Adherence Scale (FIPAS). This measure is designed to assess therapist adherence to the Kuipers et al. (2002) family intervention in psychosis treatment manual. Reliability ratings were based on a sample of thirteen audiotapes drawn from a randomized controlled trial of family intervention. The results indicated that the majority of items of the FIPAS had acceptable levels of inter-rater reliability. The findings are discussed in terms of their implications for the training and monitoring of the effectiveness of practitioners for family interventions in psychosis.

A review of instruments developed to measure outcomes for carers of people with mental health problems

Objective: Community-based care for mental disorders places considerable burden on families and carers. Measuring their experiences has become a priority, but there is no consensus on appropriate instruments. We aimed to review instruments carers consider relevant to their needs and assess evidence for their use. Method: A literature search was conducted for outcome measures used with mental health carers. Identified instruments were assessed for their relevance to the outcomes identified by carers and their psychometric properties. Results: Three hundred and ninety two published articles referring to 241 outcome measures were identified, 64 of which were eligible for review (used in three or more studies). Twenty-six instruments had good psychometric properties; they measured (i) carers' well-being, (ii) the experience of caregiving and (iii) carers' needs for professional support. Conclusion: Measures exist which have been used to assess the most salient aspects of carer outcome in mental health. All require further work to establish their psychometric properties fully.

A fuzzy multi-criteria decision approach for enhanced auto-tracking of seismic events

The main activity carried out by the geophysicist when interpreting seismic data, in terms of both importance and time spent is tracking (or picking) seismic events. in practice, this activity turns out to be rather challenging, particularly when the targeted event is interrupted by discontinuities such as geological faults or exhibits lateral changes in seismic character. In recent years, several automated schemes, known as auto-trackers, have been developed to assist the interpreter in this tedious and time-consuming task. The automatic tracking tool available in modem interpretation software packages often employs artificial neural networks (ANN's) to identify seismic picks belonging to target events through a pattern recognition process. The ability of ANNs to track horizons across discontinuities largely depends on how reliably data patterns characterise these horizons. While seismic attributes are commonly used to characterise amplitude peaks forming a seismic horizon, some researchers in the field claim that inherent seismic information is lost in the attribute extraction process and advocate instead the use of raw data (amplitude samples). This paper investigates the performance of ANNs using either characterisation methods, and demonstrates how the complementarity of both seismic attributes and raw data can be exploited in conjunction with other geological information in a fuzzy inference system (FIS) to achieve an enhanced auto-tracking performance.

Efficient fuzzy control of a rotary inverted pendulum based on LQR mapping

This paper develops fuzzy methods for control of the rotary inverted pendulum, an underactuated mechanical system. Two control laws are presented, one for swing up and another for the stabilization. The pendulum is swung up from the vertical down stable position to the upward unstable position in a controlled trajectory. The rules for the swing up are heuristically written such that each swing results in greater energy build up. The stabilization is achieved by mapping a stabilizing LQR control law to two fuzzy inference engines, which reduces the computational load compared with using a single fuzzy inference engine. The robustness of the balancing control is tested by attaching a bottle of water at the tip of the pendulum.

Kernel classifier construction using orthogonal forward selection and boosting with Fisher ratio class separability measure

A greedy technique is proposed to construct parsimonious kernel classifiers using the orthogonal forward selection method and boosting based on Fisher ratio for class separability measure. Unlike most kernel classification methods, which restrict kernel means to the training input data and use a fixed common variance for all the kernel terms, the proposed technique can tune both the mean vector and diagonal covariance matrix of individual kernel by incrementally maximizing Fisher ratio for class separability measure. An efficient weighted optimization method is developed based on boosting to append kernels one by one in an orthogonal forward selection procedure. Experimental results obtained using this construction technique demonstrate that it offers a viable alternative to the existing state-of-the-art kernel modeling methods for constructing sparse Gaussian radial basis function network classifiers. that generalize well.