How is the diagnosis of asthma in older people established in clinical practice?

What we need to know
• How is breathlessness perceived and defined in older people?
• What impact does breathlessness have on quality of life?
• Can history taking and physical examination be tailored to efficiently cover all the organ systems associated with
breathlessness?
• Can a self- or carer-rated questionnaire be used to identify asthma in patients with breathlessness?
What we need to do
• Develop self- and carer-rated questionnaires that measure change in function and quality of life before and after
treatment.
• Validate objective measures of physical function and airflow that are sufficiently sensitive to measure change with
treatment.
• Develop a diagnostic guideline in general practice that includes measures of mood and cognitive function and
involves carers where necessary.
• Provide rehabilitation and restorative care services.

Bayesian networks and decision trees in the diagnosis of female urinary incontinence

This study compares the effectiveness of Bayesian networks versus Decision Trees in modeling the Integral Theory of Female Urinary Incontinence diagnostic algorithm. Bayesian networks and Decision Trees were developed and trained using data from 58 adult women presenting with urinary incontinence symptoms. A Bayesian Network was developed in collaboration with an expert specialist who regularly utilizes a non-automated diagnostic algorithm in clinical practice. The original Bayesian network was later refined using a more connected approach. Diagnoses determined from all automated approaches were compared with the diagnoses of a single human expert. In most cases, Bayesian networks were found to be at least as accurate as the Decision Tree approach. The refined Connected Bayesian Network was found to be more accurate than the Original Bayesian Network accurately discriminated between diagnoses despite the small sample size. In contrast, the Connected and Decision Tree approaches were less able to discriminate between diagnoses. The Original Bayesian Network was found to provide an excellent basis for graphically communicating the correlation between symptoms and laxity defects in a given anatomical zone. Performance measures in both networks indicate that Bayesian networks could provide a potentially useful tool in the management of female pelvic floor dysfunction. Before the technique can be utilized in practice, well-established learning algorithms should be applied to improve network structure. A larger training data set should also improve network accuracy, sensitivity, and specificity.

Working around a contested diagnosis : borderline personality disorder in adolescence

This discourse analytic study sits at the intersection of everyday communications with young people in mental health settings and the enduring sociological critique of diagnoses in psychiatry. The diagnosis of borderline personality disorder (BPD) is both contested and stigmatized, in mental health and general health settings. Its legitimacy is further contested within the specialist adolescent mental health setting. In this setting, clinicians face a quandary regarding the application of adult diagnostic criteria to an adolescent population, aged less than 18 years. This article presents an analysis of interviews undertaken with Child and Adolescent Mental Health Services (CAMHS) clinicians in two publicly funded Australian services, about their use of the BPD diagnosis. In contrast with notions of primacy of diagnosis or of transparency in communications, doctors, nurses and allied health clinicians resisted and subverted a diagnosis of BPD in their work with adolescents. We delineate specific social and discursive strategies that clinicians displayed and reflected on, including: team rules which discouraged diagnostic disclosure; the lexical strategy of hedging when using the diagnosis; the prohibition and utility of informal ‘borderline talk’ among clinicians; and reframing the diagnosis with young people. For clinicians, these strategies legitimated their scepticism and enabled them to work with diagnostic uncertainty, in a population identified as vulnerable. For adolescent identities, these strategies served to forestall a BPD trajectory, allowing room for troubled adolescents to move and grow. These findings illuminate how the contest surrounding this diagnosis in principle is expressed in everyday clinical practice.

Training improves interobserver reliability for the diagnosis of scaphoid fracture displacement

Background The diagnosis of displacement in scaphoid fractures is notorious for poor interobserver reliability.

Questions/purposes We tested whether training can improve interobserver reliability and sensitivity, specificity, and accuracy for the diagnosis of scaphoid fracture displacement on radiographs and CT scans.

Methods Sixty-four orthopaedic surgeons rated a set of radiographs and CT scans of 10 displaced and 10 nondisplaced scaphoid fractures for the presence of displacement, using a web-based rating application. Before rating, observers were randomized to a training group (34 observers) and a nontraining group (30 observers). The training group received an online training module before the rating session, and the nontraining group did not. Interobserver reliability for training and nontraining was assessed by Siegel’s multirater kappa and the Z-test was used to test for significance.

Results There was a small, but significant difference in the interobserver reliability for displacement ratings in favor of the training group compared with the nontraining group. Ratings of radiographs and CT scans combined resulted in moderate agreement for both groups. The average sensitivity, specificity, and accuracy of diagnosing displacement of scaphoid fractures were, respectively, 83%, 85%, and 84% for the nontraining group and 87%, 86%, and 87% for the training group. Assuming a 5% prevalence of fracture displacement, the positive predictive value was 0.23 in the nontraining group and 0.25 in the training group. The negative predictive value was 0.99 in both groups.

Conclusions Our results suggest training can improve interobserver reliability and sensitivity, specificity and accuracy for the diagnosis of scaphoid fracture displacement, but the improvements are slight. These findings are encouraging for future research regarding interobserver variation and how to reduce it further.

A hybrid intelligent system and its application to fault detection and diagnosis

This paper proposes a hybrid system that integrates the SOM (Self Organizing Map) neural network, the kMER (kernel-based Maximum Entropy learning Rule) algorithm and the Probabilistic Neural Network (PNN) for data visualization and classification. The rationales of this hybrid SOM-kMER-PNN model are explained, and the applicability of the proposed model is demonstrated using two benchmark data sets and a real-world application to fault detection and diagnosis. The outcomes show that the hybrid system is able to achieve comparable classification rates when compared to those from a number of existing classifiers and, at the same time, to produce meaningful visualization of the data sets.

Application of an adaptive neural network with symbolic rule extraction to fault detection and diagnosis in a power generation plant

Artificial neural networks have a good potential to be employed for fault diagnosis and condition monitoring problems in complex processes. In this paper, the applicability of the fuzzy ARTMAP (FAM) neural network as an intelligent learning system for fault detection and diagnosis in a power generation plant is described. The process under scrutiny is the circulating water (CW) system, with specific attention to the conditions of heat transfer and tube blockage in the CW system. A series of experiments has been conducted systematically to investigate the effectiveness of FAM in fault detection and diagnosis tasks. In addition, a set of domain rules has been extracted from the trained FAM network so that its predictions can be explained and justified. The outcomes demonstrate the benefits of employing FAM as an intelligent fault detection and diagnosis tool with an explanatory capability for monitoring and diagnosing complex processes in power generation plants.

Fault detection and diagnosis of induction motors using motor current signature analysis and a hybrid FMM-CART model

In this paper, a novel approach to detect and classify comprehensive fault conditions of induction motors using a hybrid fuzzy min-max (FMM) neural network and classification and regression tree (CART) is proposed. The hybrid model, known as FMM-CART, exploits the advantages of both FMM and CART for undertaking data classification and rule extraction problems. A series of real experiments is conducted, whereby the motor current signature analysis method is applied to form a database comprising stator current signatures under different motor conditions. The signal harmonics from the power spectral density are extracted as discriminative input features for fault detection and classification with FMM-CART. A comprehensive list of induction motor fault conditions, viz., broken rotor bars, unbalanced voltages, stator winding faults, and eccentricity problems, has been successfully classified using FMM-CART with good accuracy rates. The results are comparable, if not better, than those reported in the literature. Useful explanatory rules in the form of a decision tree are also elicited from FMM-CART to analyze and understand different fault conditions of induction motors.