The relationship between intellectual disability, Indigenous status and risk of reoffending in juvenile offenders on community orders.

BACKGROUND: Intellectual disability (ID), age and aboriginal status have been independently implicated as risk factors for offending to varying degrees. This study examined the relationship between age, ID and the Indigenous status of juvenile offenders. It also examined the outcomes of the sample's offending in terms of court appearances and sentencing, criminogenic needs and risk of reoffending. METHOD: The sample comprised 800 juvenile offenders on community orders of whom 19% were Indigenous, who completed the New South Wales Young People on Community Order Health Survey between 2003 and 2005. Risk and criminogenic needs were evaluated using the Youth Level of Service/Case Management Inventory (Australian Adaptation) (YLS/CMI: AA). RESULTS: Those with an ID were found to have a higher risk of reoffending than those without an ID. Those with an ID were also more likely to be younger and Indigenous. For Indigenous young offenders, there was no difference between those with and without an ID in risk category allocation or number of court dates. For non-Indigenous young offender, those with an ID had higher risk scores and more court dates. CONCLUSIONS: This study provided evidence that Indigenous status may play a significant role in the relationship between ID and offending in juvenile offenders on community orders. These findings have clear implications for the 'risk', 'needs' and 'responsivity' principles of offender classification for treatment. Emphasis is placed on the requirement for addressing the needs of Indigenous juvenile offenders with an ID.

Applying Data Preprocessing Methods to Predict Premature Birth.

Data mining and pattern classification tools have{enabled prediction of several medical outcomes with high levels of accuracy. This is due to the capability of handling large datasets, even those with missing values. Preterm birth (PTB) can have damaging long-term effects for infants and rates have been increasing over the last two decades worldwide. The purpose of this work was to investigate whether preprocessing methods, when applied to two different prenatal datasets, can improve prediction accuracy of our software tool to predict PTB. The primary software used within this work was R. The software was used to deal with missing values and class imbalances found in these two datasets. The results show that in comparison to our past work, we have managed to increase the performance of the prediction tool using the metrics of sensitivity, specificity, and ROC values.

Oxytocin augmentation of labor and perinatal outcome in nulliparas.

Recent pharmacologic observations in vivo suggest the use of a lower starting dose (0.5-0.1 mU/minute) of oxytocin and a longer interval between dose augmentations (30-60 minutes) than previously advocated. In this study, a high-dose oxytocin protocol was used to augment nonprogressive labor in normal nulliparous women. The rate of oxytocin infusion started at 6 mU/minute and was increased by 6 mU/minute every 15 minutes to a maximum dose of 40 mU/minute. Charts were reviewed of 1080 nulliparous women for whom the principles of active management of labor were followed and delivery occurred between March 1, 1986 and December 31, 1988. Four hundred fifty-six who required oxytocin augmentation in labor were compared with 624 who did not receive oxytocin. There were no statistically significant differences in birth asphyxia or perinatal morbidity.

Results of an international survey of clinical engineering departments. Part 2. Budgets, staffing, resources and financial strategies.

Data collected from clinical engineering departments in Canada, the USA, the European Economic Community and two Nordic countries, Sweden and Finland, have led to an evaluation of their budget, staffing and other resource levels. Financial strategies to support their role are proposed.

Clinical decision-support systems for intensive care units using case-based reasoning.

The artificial intelligence approach used in this work focusses on case-based reasoning techniques for the estimation of medical outcomes and resource utilization. The systems were designed with a view to help medical and nursing personnel to assess patient status, assist in making a diagnosis, and facilitate the selection of a course of therapy. The initial prototype provided information on the closest-matching patient cases to the newest patient admission in an adult intensive care unit (ICU). The system was subsequently re-designed for use in a neonatal ICU. The results of a short clinical pilot evaluation performed in both adult and neonatal units are reported and have led to substantial improvement of the prototype. Future work will include longer-term clinical trials for both adult and neonatal ICUs, once all the software changes have been made to both prototypes in response to the comments of the users made during the preliminary evaluations. To date, the results are very encouraging and physician interest in the potential clinical usefulness of these two systems remains high, and particularly so in the new testing environment in Ottawa.

Clinical decision support systems for intensive care units: using artificial neural networks.

The paper provides an overview of applications of artificial neural networks (ANNs) to various medical problems, with a particular focus on the intensive care unit environment (ICU). Several technical approaches were tested to see whether they improve the ANN performance in estimating medical outcomes and resource utilization in adult ICUs. These experiments include: (1) use of the weight-elimination cost function; (2) use of 'high' and 'low' nodes for input variables; (3) verifying the effect of the total number of input variables on the results; (4) testing the impact of the value of the constant predictor on the performance of the ANNs. The developments presented intend to help medical and nursing personnel to assess patient status, assist in making a diagnosis, and facilitate the selection of a course of therapy.

Influence of missing values on artificial neural network performance.

The problem of databases containing missing values is a common one in the medical environment. Researchers must find a way to incorporate the incomplete data into the data set to use those cases in their experiments. Artificial neural networks (ANNs) cannot interpret missing values, and when a database is highly skewed, ANNs have difficulty identifying the factors leading to a rare outcome. This study investigates the impact on ANN performance when predicting neonatal mortality of increasing the number of cases with missing values in the data sets. Although previous work using the Canadian Neonatal Intensive Care Unit (NICU) Network s database showed that the ANN could not correctly classify any patients who died when the missing values were replaced with normal or mean values, this problem did not arise as expected in this study. Instead, the ANN consistently performed better than the constant predictor (which classifies all cases as belonging to the outcome with the highest training set a priori probability) with a 0.6-1.3% improvement over the constant predictor. The sensitivity of the models ranged from 14.5-20.3% and the specificity ranged from 99.2- 99.7%. These results indicate that nearly 1 in 5 babies who will eventually die are correctly classified by the ANN, and very few babies were incorrectly identified as patients who will die. These findings are important for patient care, counselling of parents and resource allocation.

Investigation into the past and future of women in science and engineering.

Covering the Ancient Greek era, the Middle Ages, the Renaissance, the Enlightenment, the 19th and 20th C., this paper explores the visions of the abilities of women, their access to education, and their roles in these epochs. Recent data on the participation rate of women in science and engineering, the culture in these fields, and strategies to increase their presence are discussed. The paper ends with a discussion on how science and engineering could benefit from integrating and valuing a blend of masculine and feminine perspectives. Biomedical engineering as a field frequently chosen by women is mentioned.

Bayesian-based diagnostic decision-support for pediatrics.

The goal of this project was to develop a Pediatric Decision Support system (PDS) that allows a resident physician to define a patient case based on symptoms (diagnostic signs and test results) and generates a list of possible diagnoses based on the World Health Organization's International Classification of Diseases (ICD10). The intent is to improve the diagnostic approach taken by resident physicians and eventually become a training tool in medical education programs.

Segmentation and landmark identification in infrared images of the human body.

The segmentation and landmark identification in infrared images of the human body are key steps in a computerized processing of large database of thermal images. The segmentation task is especially challenging due to specific characteristics of thermal images. Few papers deal with segmentation techniques for clinical infrared images and available segmentation methods (e.g. for breast or military thermal images) do not perform well on other types of images. This paper presents a few strategies for the automated segmentation and registration of anatomical landmarks on thermal images of arms and hands. The segmentation method is based on mathematical morphological operations and simple rule based processing easily available through prior knowledge about the objects of interest.

An Analysis Of Chirpp Data To Predict Severe ATV Injuries Using Artificial Neural Networks.

This paper describes the development of a tool to predict the severity of all-terrain vehicle (ATV) injuries using artificial neural networks (ANNs). The data was obtained from the Canadian Hospitals Injury Reporting and Prevention Program (CHIRPP). The main objective of the study was to identify the contribution of input variables in predicting severe injury or death. An ANN architecture with 9 hidden nodes and one hidden layer resulted in optimal performance: a logarithmic-sensitivity index of 0.099, sensitivity of 47.3%, specificity of 80.8%, correct classification rate (CCR) of 68.6% and receiver operating curve (ROC) area of 0.711. The minimum data set that can help predict injury severity is discussed.

Evolution of the surface temperature of pianists' arm muscles using infrared thermography.

Musculoskeletal disorders are very frequent among musicians. Diagnosis is difficult due to the lack of objective tests and the multiplicity of symptoms. Treatment is also problematic and often requires that the musician stop playing. Most of these disorders are inflammatory in nature, and therefore involve temperature changes in the affected regions. Temperature measurements were recorded with an infrared camera. In this paper we present an overview of the temperature measurements made in the arms of 8 pianists during regular piano practice sessions.

Clinical engineering in today's hospital: perspectives of the administrator and the clinical engineer.

In 1987-88, the first of two surveys conducted questioned the administrator's viewpoint on choice of reporting authority for plant operations and clinical engineering departments as well as the job satisfaction and prestige associated with these responsibilities. The second tested the response of clinical engineers on similar issues as well as the effect of certain organizational factors on their degree of functional involvement in the equipment-management process. In the first survey, two-thirds of the administrators chose a structure that, as shown in the second survey, leads to a higher degree of involvement and satisfaction for clinical engineers. Other organizational factors that have an effect are: the type of hospital (teaching and nonteaching), the presence of qualified university-degree engineers, and ensuring that the clinical engineering role within the health care institution is recognized and supported. Teaching hospitals are found to provide a better climate than nonteaching ones for the support of the research and education activities. Clinical engineering departments, whose role has been recognized and supported by their institution, are more substantially involved in all aspects of the equipment-management process than those who are still seeking this recognition. Finally, departments where university-degree engineers have been hired again show more involvement and commitment to the quality and efficiency of their operation.

Decision-support and intelligent tutoring systems in medical education.

One of the challenges in medical education is to teach the decision-making process. This learning process varies according to the experience of the student and can be supported by various tools. In this paper we present several approaches that can strengthen this mechanism, from decision-support tools, such as scoring systems, Bayesian models, neural networks, to cognitive models that can reproduce how the students progressively build their knowledge into memory and foster pedagogic methods.